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AI for Information Security call for proposals — Spring 2024

Advancing possible solutions for some of the most challenging problems in information security.

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About this CFP

AWS is committed to helping customers achieve the highest levels of security in the cloud. Our security services use cutting-edge machine learning (ML) algorithms to improve the security posture of AWS accounts. We aim to continue advancing possible solutions for some of the most challenging problems in information security. We are seeking to fund machine learning research on the following topics in information security:

  1. Threat, intrusion, and anomaly detection for cloud security
  2. Generative AI and foundation models for information security
  3. Graph modeling and anomaly detection on graphs
  4. Learning with limited/noisy labels and weakly supervised learning
  5. ML for malware analysis and detection
  6. Finding security vulnerabilities using ML
  7. Causal inference for information security
  8. Zero/one-shot learning for information security
  9. Reinforcement learning for information security
  10. Protecting and preserving data privacy in the cloud
  11. Securing generative AI and foundation models

Timeline

Submission period: March 27 to May 7, 2024 (11:59PM Pacific Time).
Decision letters will be sent out August 2024.

Award details

Selected Principal Investigators (PIs) may receive the following:

  1. Unrestricted funds, no more than $80,000 USD on average
  2. AWS Promotional Credits, no more than $40,000 USD on average

Awards are structured as one-year unrestricted gifts. The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel.

Eligibility requirements

Please refer to the ARA Program rules on the FAQ page.

Proposal requirements

Proposals should be prepared according to the proposal template.

Selection criteria

AI for Information Security will make the funding decisions based on the potential impact to the research community and quality of the scientific content.

Expectations from recipients

To the extent deemed reasonable, Award recipients should acknowledge the support from ARA. Award recipients will inform ARA of publications, presentations, code and data releases, blogs/social media posts, and other speaking engagements referencing the results of the supported research or the Award. Award recipients are expected to provide updates and feedback to ARA via surveys or reports on the status of their research. Award recipients will have an opportunity to work with ARA on an informational statement about the awarded project that may be used to generate visibility for their institutions and ARA.

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Research Scientist, Pricing Science
US, WA, Seattle
Pricing is one of the most consequential decisions Amazon makes, and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science team builds the machine learning (ML) systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value (LTV) frameworks, and the experimentation infrastructure that validates whether pricing changes actually work. We're looking for a Research Scientist to own causal inference at the intersection of ML and pricing experimentation. Our team has identified a real gap: the methodological bridge between econometric analysis and production-scale ML pipelines needs a practitioner who lives in both worlds. You'll build conditional average treatment effect (CATE) estimation models, design analysis workflows for pricing experiments, and develop reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. If you're a scientist who wants to tackle hard causal identification problems in a high-stakes production environment, and who finds satisfaction in making rigorous methods accessible to a broader team, this role is for you. Key job responsibilities - Design, train, evaluate, and deploy end-to-end causal ML pipelines for pricing use cases, including CATE estimation models and heterogeneous treatment effect frameworks. - Serve as the team's causal ML methodology expert — defining identification strategies, model selection criteria, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. - Contribute causal analysis methodology to pricing A/B test post-analysis and build reusable tooling that economists and other non-ML scientists can adopt without requiring deep ML expertise. - Assess emerging causal inference methods (e.g., synthetic difference-in-differences, generalized random forests, causal representation learning) for applicability to Amazon's pricing context, and author internal methodology proposals for adoption. - Partner with senior economists on identification strategy and causal assumptions, collaborate with software and data engineering partners on production deployment, and align with product managers on experiment design requirements. A day in the life Your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given week you might investigate an optimization anomaly in simulation and trace it to an unmodeled market dynamic, then design an offline evaluation framework to benchmark competing approaches before committing to online testing. You'll collaborate with senior economists on identification strategy for a pricing experiment, present a science proposal for incorporating a new competitiveness signal into an optimization system, or work with the experimentation platform team on randomization design. You'll also have opportunities to write up novel scientific findings for submission to venues such as KDD, NeurIPS, or the ACM Conference on Economics and Computation. About the team The P2 Optimization Science team sits at the center of Amazon's pricing decisions. We build the ML systems, causal inference pipelines, and experimentation infrastructure that determine how prices are set across Amazon's global catalog. Our team brings together research scientists, economists, and engineers who collaborate closely to turn rigorous methodology into production-quality systems with measurable business impact. We value scientific rigor, clear communication across disciplines, and a culture where every team member's work is designed to be extensible and useful beyond their own project. If you want your science to directly influence how hundreds of millions of customers experience Amazon, this is the team to join.
Applied Scientist, Center for Quantum Computing
US, CA, San Francisco
Amazon’s Center for Quantum Computing (CQC) is building the next generation of quantum computing hardware. The Center for Quantum Computing (CQC) is our longest-horizon bet and operates at the intersection of hardware and physics. The goal is a quantum computer that could solve in hours what today's most robust machines would take thousands of years to compute, unlocking breakthroughs in myriad of areas ranging from advanced materials for batteries to cryptography and fundamentally-secure data sharing protocols. This program involves R&D in device theory, materials science, nano-scale fabrication, superconducting circuit design, cryogenic refrigeration, and quantum algorithms and applications. Currently, the quantum hardware team is focused on improving the underlying chip fabrication of the quantum processor and the scaling of the control system needed to run large-scale quantum circuits. As part of this effort, the team is expanding its chip fabrication capabilities through the construction of a new cleanroom facility dedicated to superconducting quantum chip fabrication, and working with strategic partners to develop scaled versions of the control electronics, I/O wiring, and cryogenic refrigeratation needed for utility-scale quantum computers. We operate with a startup-like pace inside one of the world's most capable technology organizations, combining the urgency of a competitive frontier with the resources and infrastructure of pan Amazon. We are seeking an Applied Scientist III to join our team and drive the design, construction, and operation of quantum processors. You will work alongside world-class physicists and engineers to push the frontier of quantum computing — from laser system design and atom trapping to high-fidelity gate execution and quantum error correction. This is a rare opportunity to be part of shaping a technology that could define the next era of computing. Key job responsibilities - Design, build, and operate quantum computing experiments, including optical trapping arrays, laser cooling and state-preparation systems, and high-fidelity gate-driving optics - Develop and optimize two-qubit entangling gates using interactions, targeting fidelities at or beyond the current state of the art (>99.5%) - Implement and characterize erasure-detection protocols leveraging the metastable-state structure of ¹⁷¹Yb to achieve hardware-native erasure conversion rates >90% - Contribute to quantum error correction experiments, including surface codes and quantum low-density parity-check (qLDPC) codes, and work toward demonstrating error suppression factors (Λ) well beyond current published records - Design and improve atom-array loading, rearrangement, and continuous replenishment techniques to support registers of thousands to tens of thousands of atoms - Develop control software and calibration routines for high-speed experimental sequences, including real-time feedback and adaptive protocols - Analyze experimental data, build physical models to explain observed behavior, and propose and execute improvements to system performance - Collaborate with theory, software, and hardware engineering teams to co-design quantum architectures, error-correcting codes, and decoder systems - Publish results in leading scientific journals and present at top conferences to maintain Amazon's visibility in the quantum research community
Imitation Learning Engineer, RIVR
CH, Zurich
RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software to hardware, RIVR is purpose-built for safety, reliability, and the customer from day one. Imitation learning enables our robot to mimic "expert" behaviors, derived from human demonstrations or algorithmic strategies. By utilizing state-of-the-art generative AI and similar methods, our wheeled-legged robot can significantly enhance its autonomy and manipulation skills. In this role, you will enable robots to autonomously generate actions from demonstrations and real-time sensor data. These processes may also incorporate responses to natural language commands, further advancing the robot's skills. We are seeking an expert in imitation learning and generative AI techniques that directly produce robot behaviors, along with a deep knowledge of both supervised and self-supervised learning algorithms. If you are passionate about pushing the boundaries of AI and eager to deliver innovative solutions, we invite you to join us in shaping the future of intelligent robotics. Key job responsibilities Develop imitation learning algorithms, such as diffusion policies, to enable robots to autonomously execute actions based on demonstrations and real-time sensor data. Design, test, and refine your algorithms to meet the demands of complex real-world autonomy and manipulation tasks. Construct a dataset for the imitation learning algorithm using human demonstrations or automated expert algorithms. Streamline the workflow to efficiently expand the imitation learning dataset with new tasks. Collaborate with the reinforcement learning team to innovate methods that leverage both simulated and real-world data. Implement deployment-ready code for the real robot, optimized for the robot’s computational constraints. Build, lead and mentor an exceptional team of software engineers. Provide expert guidance to product managers and executives for strategic decision-making. Create and maintain documentation, guidelines, and best practices to streamline knowledge sharing.
Principal Applied Scientist, Ads Measurement Science
US, NY, New York
The Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) organization of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of their ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. This Principal Applied Scientist leads the technical vision and execution for Amazon Advertising's performance measurement. Our measurement products and tools help customers understand the holistic impact of their spend on Amazon Ads and optimize their portfolios. This is a complex, multi-disciplinary scientific space spanning machine learning, causal inference, statistics and experimentation, natural language processing, and generative AI. As a GenAI-first organization, we build foundational and agentic models that power advertiser use cases across Ads, while empowering our Applied Scientists to directly build and ship products. You will guide a team of scientists with expertise across different domains to come together and innovate on some of the hardest problems in advertising, made more challenging by evolving privacy regulations and technological changes. In measuring the comprehensive impact of advertising, key challenges you will tackle include capturing impact: 1) both for identified and increasingly unidentified web traffic in light of an evolving global privacy landscape; 2) for outcomes across retailers, including both within and beyond Amazon; and 3) for both short- and long-term impacts of advertising. Key job responsibilities * Driving the scientific vision of the teams in your organization and you advise and influence its technical leadership on performance measurement models and products. * Identifying, tackling, and proposing innovative solutions to intrinsically hard, previously unsolved problems. You solve problems pragmatically, applying judgment and experience to balance trade-offs between competing interests. * Bringing clarity to complex problems, probing assumptions, illuminating pitfalls, fostering shared understanding, and guiding towards effective solutions. * Serving and being recognized by internal and external peers as a thought leader in modeled measurement in Ads. You will engage routinely with the broader scientific community, e.g. as a reviewer, program committee member, workshop organizer, or chair at peer-reviewed internal/external conferences. * Leading by example, with your papers, analyses, and code setting the standard in your organization for scientific engineering excellence. * Leading science and design reviews, aligning teams across your organization around coherent algorithmic strategies. * Influencing your team’s science and business strategy by driving one or more team roadmaps contributing to the organization’s roadmap and taking responsibility for some organizational goals. You drive multiple new product features from inception to production launch. * Guiding the career development of others, actively mentoring and educating the larger applied science community on trends, technologies, and best practices. You drive the documentation of scientific innovation. You attract bar-raising talent from academia and industry. * Staying abreast of academic and industry trends, knowing the state of the art, and building on top of existing solutions instead of reinventing when possible. About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
Robotics Applied Scientist
US, CA, Sunnyvale
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building robotic systems that combine frontier AI, sophisticated control, and advanced mechanical design to create adaptable automation capable of working safely alongside humans in dynamic environments. This is an opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. We are seeking an Applied Scientist to develop tactile- and force-driven manipulation policies for highly dexterous, multi-fingered robotic hands and grippers. In this role you will research and build policy learning algorithms that enable robotic hands to perform grasping and in-hand manipulation in unstructured, real-world environments, using touch and force feedback as first-class signals rather than afterthoughts. Much of this work is grounded in reinforcement learning. To be successful you need to be highly motivated, dive deep, and deliver to the highest standards. You will demonstrate strong working knowledge of modern policy learning methods, with expertise in taking algorithms from simulation onto real hardware. That means being comfortable working against real physical constraints and treating sim-to-real as a core research problem rather than optimizing for benchmark numbers alone. You will work across tactile signal processing, contact-rich manipulation, sim-to-real transfer, and multi-modal sensor fusion to build robust solutions for autonomous grasping, dexterous re-orientation, and fine motor control. You will bring the desire to learn from new challenges, and the problem-solving and communication skills to work within a highly interactive and experienced team. Key job responsibilities - Design and train manipulation policies that leverage tactile and force feedback for dexterous grasping, in-hand manipulation, and contact-rich tasks, using reinforcement learning, imitation learning, or hybrid approaches. - Drive sim-to-real transfer, closing the gap between simulated and physical performance on real robotic hands. - Shape objectives and control strategies with the hardware in mind, accounting for how motors and actuators actually behave: transmission ratios, torque versus power, backdrivability, and thermal limits. - Build simulation-based and on-robot evaluation frameworks, with benchmarks and metrics that make tactile perception and policy performance systematically comparable across iterations. - Own scientific and technical projects within the tactile sensing and manipulation workstreams, driving from research concept through deployment on physical robotic systems — and, at the senior level, set direction across a workstream. - Collaborate with hardware, mechanical design, firmware, and controls teams — and with partner applied science organizations — to translate research advances into deployable robotic capabilities, and to inform sensor and actuator design decisions with policy-level requirements. - Publish at top-tier venues and build collaborations with the external research community. - Contribute to a strong scientific bar on the team through code and design review, and mentor engineers and interns working on real-world manipulation and sensing problems. A day in the life Your morning might start with a standup alongside hardware and controls engineers reviewing overnight sim-to-real training runs on a multi-finger gripper, debugging why a grasp policy that worked in simulation is slipping on a real sensor array. Mid-morning you join the weekly tactile sensing workstream to align on sensor integration milestones and share early results from a contact-state estimator you have been prototyping. After lunch you spend a focused block iterating on a reinforcement learning reward formulation, testing variations in simulation before queuing runs on the cluster. Later you pair with a mechanical engineer to review sensor placement trade-offs on an upcoming gripper revision, then wrap the day by drafting a short experiment write-up and sketching next steps for a conference submission. No two days look exactly the same: one week you may be collecting teleoperated demonstrations on the physical robot to seed an imitation learning pipeline; the next you could be deep in a codebase refactor to support a new tactile modality. Throughout, you balance hands-on research — writing algorithms, running experiments, analyzing data — with cross-team collaboration and mentoring. The common thread is moving from research insight to working capability on real hardware, with a tight feedback loop between simulation and the physical world. About the team Our is developing next-generation manipulation capabilities for highly dexterous multi-fingered grippers and hands. Our work spans tactile sensor development, manipulation policy learning, and the tight integration of sensing hardware with intelligent control software. We are building systems that can feel, adapt, and manipulate with human-level dexterity. You will join a team working at the frontier of tactile sensing and contact-rich manipulation, helping define how robots perceive and interact with objects through touch. Because we build the hands as well as the policies that drive them, you will have a direct line to the sensor, actuator, and mechanism designs your algorithms depend on — and real influence over how they evolve. This is an opportunity to solve hard problems across perception, learning, and physical interaction alongside a team of scientists and engineers building toward real-world deployment.
Senior Applied Scientist, Amazon Leo, Amazon Leo Networking Software
US, WA, Redmond
Amazon Leo is building a low Earth orbit satellite broadband network to deliver fast, affordable connectivity to customers around the world, including those underserved by traditional infrastructure. Operating a live constellation and its ground network at scale means service disruptions are an inevitable reality, and how quickly we detect them, understand them, and prevent them from recurring defines the trust we earn as a connectivity provider. We are seeking a talented Senior Applied Scientist to build the next generation of observability and availability science for the Leo network. Leo already detects outages, notifies affected customers, and restores service. The frontier is prediction and prevention: reasoning over complex network and ground-segment telemetry to autonomously detect anomalies before they reach customers, attribute root cause automatically across a sprawling event surface, and rationalize a large, still-maturing metric set so responders trust what the system tells them. In this role you will work backwards from the needs of customers and operations teams. You will own the scientific direction end to end, partnering closely with network engineers, the Network Operations Center, and the Incident Management team, and the Software and Networking orginization to translate a long-term science vision into concrete research and delivery roadmaps. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities * Own the observability and availability science end to end, from long-term vision to research roadmap to production delivery, working backwards from the needs of customers, the NOC, and network engineering. * Develop time-series and graph-based models with dynamic baselining that detect network and ground-segment anomalies before they cross the High Severity threshold, enabling proactive investigation rather than reactive response. * Reason over complex, multi-system telemetry to attribute cause automatically during live HSEs and LSEs, so Call Leaders and engineers spend time mitigating, not hunting. * Provide the science behind the network's bucketed availability decomposition (geometric and satellite coverage, ground and network segments, customer terminals, payload, and planning), attributing every movement to a true cause rather than noise, so leadership steers on signal and engineering effort lands where it actually moves availability and throughput. * Deliver the models, analyses, and metrics that feed the daily system readiness and validation review consumed by senior leadership and product and engineering, turning availability and throughput data into a defensible, evidence-based read on progress against GA entry criteria. * Own the recurring, fleet-wide "hot topic" investigations that span ASIC, firmware, payload, network, and planning, separating correlated symptoms from true root cause across thousands of nodes, quantifying how much availability or throughput each is costing, and answering whether it is a launch-blocking risk and whether it is improving. * Deliver alert correlation and deduplication, false-positive reduction, and metric-quality scoring so responders trust what the system tells them, directly supporting a large, still-maturing KPI set. * Tie your science to the unit the org actually measures: reduce detection and attribution time, raise availability against the network's bar, and increase the share of events prevented before customer impact. * Partner day to day with the Network Operations Center, Network Engineering, and the Incident Management (Call Leader) program; sit on live HSE/LSE bridges as the science partner and turn event learnings into model improvements. * Feed COEs and RCAs back into detection and attribution so recurring signatures are caught earlier, and help design and score gamedays that stress-test both the network and your models against real, closed events. * Build and deploy production pipelines on AWS (e.g., SageMaker, Bedrock, Glue) with model validation, monitoring, drift detection, and explainability, because operations teams must be able to trust and act on model output under pressure. * Set data-science standards, mentor scientists, and drive best practices across the Leo networking organization as the science function scales. * Produce explainable output and written narratives that let responders and leaders three or more levels up act on your models during and between events.
Data Scientist, OTS - Data ANCHOR Team
US, TX, Austin
The OTS ANCHOR (Analytics, Insights, and Centralized Hub for OTS Reporting) team is part of the WW IT Operations & RME organization, responsible for delivering data solutions that drive operational excellence across Amazon's global IT infrastructure. We build data pipelines, predictive models, develop analytical intelligence frameworks, and create AI-powered solutions that transform how Amazon manages, triages, and resolves IT operational incidents at scale pointing towards the Letting Data Tell the Story north start vision. We are seeking a Data Scientist to help own the end-to-end data science charter for critical operational workstreams — from forecasting IT service demand and capacity planning to designing intelligent severity triaging models and building the analytical backbone for agentic AI solutions. You will work backwards from complex operational problems to create models and solutions that directly impact the efficiency and reliability of Amazon's IT operations worldwide. Key job responsibilities - Design, develop, and deploy machine learning models for IT operations forecasting, including incident volume prediction, capacity planning, and workforce demand modeling - Build and maintain severity classification and intelligent triaging models that process thousands of daily IT service tickets across global regions - Develop anomaly detection systems to identify emerging patterns in operational data, supporting proactive incident prevention - Apply econometric and statistical methods to measure the impact of operational interventions and inform resource allocation decisions - Partner with engineering teams to productionize ML models via automated pipelines using AWS services (SageMaker, Step Functions, Lambda, Redshift) - Design and conduct experiments to empirically validate model performance and operational impact - Contribute to data quality monitoring frameworks and help maintain standards for operational data integrity - Collaborate with cross-functional teams (ServiceNow platform, Decision Intelligence, Field Operations) to translate analytical insights into actionable recommendations - Communicate findings, model performance, and recommendations to stakeholders and senior leaders through written documents and presentations - Contribute to the team's science roadmap, identifying opportunities to apply ML/AI (including Generative AI) to operational challenges A day in the life Amazon Benefits: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan
2027 Applied Science Internship - Robotics - United States
US, MA, North Reading
Robotics Applied Scientist II Intern/Co-op (2027) Are you passionate about robotics and research? Do you want to solve real customer problems through innovative technology? Do you enjoy working on scalable research and projects in a collaborative team environment? Do you want to see your science solutions directly impact millions of customers worldwide? We’re looking for current PhD students with a passion for robotic research and applications to join us in 2027 as Robotics Applied Scientist II Interns/Co-ops to shape the future of robotics and transform automation at Amazon’s scale. By applying to this role, you will be considered for Robotics Applied Scientist II Intern/Co-op (2027) opportunities across various Robotics teams at Amazon with different robotics research focus. Internship positions are available for multiple locations, durations (3 to 6+ months), and year-round start dates (winter, spring, summer, fall). Join us at the forefront of Applied Science, where your contributions will shape the future of Robotics! Key job responsibilities As a Robotics Applied Scientist II Intern/Co-op, you will work alongside global experts to advance the state of the art in robotics and artificial intelligence, tackling innovative problems that bridge the gap between research and real-world robotics implementation to innovate and build solutions on behalf of our customers. We are particularly interested in students with specialization and expertise in one or more of the following research areas: - Robotics - Artificial Intelligence - Robotics Manipulation (e.g., robot arm, grasping, dexterous manipulation, end of arm tools/end effector) - Autonomous Mobile Robots - Mobile Manipulation - Multi-Robot Coordination - Autonomous Navigation - Locomotion - Motion/Path Planning - Controls - Perception - Sensing - Robot Learning - Machine Learning - Computer Vision - Large Language Models (LLM), Vision-Language-Action Models (VLA), Vision-Language-Models (VLM) - World Models - Human-Robot Interaction - Robotics Simulation - Optimization - And related! Key responsibilities may include: - Develop novel, scalable algorithms and/or modeling techniques that advance the state-of-the-art in science areas such as robotics, artificial intelligence, or related - Tackle challenging, innovative research problems within robotics, artificial intelligence, or related that bridge the gap between theoretical advancement and practical robotics implementation - Collaborate with cross-functional teams to solve complex problems, and to communicate research findings clearly, leveraging your expertise in robotics, artificial intelligence, or related - Demonstrate the ability to work independently, thrive in a fast-paced, ever-changing environment - Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. - Critically review and validate outputs from AI tools and automated systems. About the team Explore Robotics Teams at Amazon At Amazon, robotics is where innovative research meets real-world deployment. There are many different specialized robotics teams, including the few highlighted below. Learn more about Robotics at Amazon here: https://www.amazon.science/research-areas/robotics Fulfillment Technologies and Robotics We build dynamic partnerships between people and intelligent machines. This intricate collaboration helps Amazon fulfill orders with unmatched accuracy. Working in symphony with our robotic technology, you’ll have the opportunity to extend your technical capabilities by working alongside some of the industry’s most advanced technologies. This includes our fleet of autonomous mobile robots, sophisticated control software, and technologies like language perception, machine learning, object recognition, and semantic understanding of commands. These technologies help us deliver an ever-improving customer and employee experience, as well as improve the safety of our facilities. Amazon Robotics Amazon Robotics is at the epicenter of robotics innovation. We design, test, and build mobile robots and mechatronic systems. Our offices, labs, test floors, and factories are close together, which means we can design the hardware, write the code, test, build, and watch it all come to life. Our areas of focus include advancements in autonomous mobility, AI, robotic management software, and much more. Fauna Fauna is focused on building capable, safe, and genuinely delightful robots for everyday life. Fauna’s goal is simple: make robots people actually want to live and interact with everyday. Fauna develops tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Fauna’s work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but overall experience. Amazon Fulfillment Technologies Amazon Fulfillment Technologies develops tech solutions that create network optimizations for all Amazon sites worldwide. We own network infrastructure and devices in all buildings and labor planning and scheduling technology for fulfillment centers in North America and Europe.
Principal Applied Scientist, FAIM (Full Funnel Agentic Intelligence and Models)
US, NY, New York
The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows. As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money." This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. In this role you will - Own the science vision for developing evaluations of the Ads Agent - Partner with scientists from across Ads to develop best practices for evaluating agent performance both online and offline - Raise the science bar for all agent evaluations developed in Ads. - Own establishing a data driven foundation for making decisions about when new features are ready to launch in Ads Agent. Key job responsibilities - Define the long-term scientific vision for agent evaluations in Ads - Translate ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues.
Data Scientist, WWGS SSG Strategic Initiative Development
CN, 31, Shanghai
As WWGS is committed to building an AI-native, agentic organization, the heart of SSG's evolution is to reinvent how we engage sellers with an AI-first mindset. Delivering that ambition at scale demands breakthrough innovation in our foundational capabilities, at ever-faster speed. To meet these priorities with the pace and focus they require, we are standing up a dedicated team, organized as a POD model - a lean, cross-functional team led by a single owner who holds end-to-end decisions and the business outcome, so we can move at the speed the business demands. Our priority is to build the capability to reach the right sellers at scale, anchored on two pillars: Seller Tagging and Good Contact. Through Seller Tagging, we will turn today's fragmented, siloed seller signals into a unified seller intelligence capability that deepens and broadens how well we truly understand every seller. Through Good Contact, we will resolve the seller identity gaps across our channels and systems, so that every engagement and every system that depends on it, can consistently reach the right seller contact. The Data Scientist will be part of the POD team and support the team to establish structured data governance and model training framework, and trail predict models with real business use cases. Key job responsibilities Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage. Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling). Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions. Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals. Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis. Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership. Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools. Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM). Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.
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Sr. Applied Scientist - CV, Vulcan Stow
US, WA, Seattle
Do you want to create the greatest-possible worldwide impact in Robotics? Amazon has the world's most exciting treasure trove of robotics challenges. At Amazon Robotics we build high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. Amazon Robotics invents and scales AI systems for robotics in fulfillment. Our mission is to enable robots to interact safely, efficiently, and fluently high density real-world fulfillment centers. Our AI solutions enable robots to learn from their own experiences, from each other, and from humans to build intelligence that feeds itself. We hire and develop subject matter experts in AI with a focus on 3D perception, computer vision, deep learning, and generative modeling. We target high-impact algorithmic unlocks in areas such as 3D scene understanding and completion, semantic occupancy prediction, multi-view 3D reconstruction, depth estimation, shape completion, and real-time inference - all of which have high-value impact for our current and future fulfillment networks. We are seeking an passionate, hands-on, seasoned Senior Applied Scientist who will be deep in code and algorithms; who is technically strong in building scalable 3D perception systems across semantic scene completion, encoder-decoder and transformer architectures (e.g., VoxFormer, MonoScene), voxelized occupancy prediction, panoptic and instance segmentation, depth estimation, point cloud processing, and multi-view fusion. As a Senior Applied Scientist, you will contribute to the research and development of advanced 3D perception pipelines that enable robots to reason about occluded and partially observed environments; your work along with other top-notch scientists and engineers will deliver the world's most scalable and robust robotic perception systems. You will drive ideas to products using paradigms such as 3D generative models, query-based transformers, masked autoencoder-style completion, and scalable pseudo-ground-truth data generation. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on challenging perception problems - such as completing 3D bin scenes from partial observations, integrating multi-camera inputs, and optimizing inference latency for edge deployment - distill requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth in 3D computer vision, scientific vision, project management skills, great communication skills, and a drive to achieve results in a collaborative team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven't been solved at scale anywhere before. Along the way, we guarantee you'll get opportunities to be a disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement 3D perception models - including encoder-decoder networks, query-based transformers, and generative architectures- for semantic occupancy prediction and scene completion on robotic platforms. Own the end-to-end model lifecycle: develop scalable training pipelines, optimize inference latency for ARM-based edge processors, and deploy production models that meet real-time performance targets. Design and scale pseudo-ground-truth data generation pipelines - both heuristic-based and learning-based (e.g., SAM3D, shape completion) to produce curated training samples using SageMaker infrastructure. Drive multi-view perception integration by fusing multiple view camera inputs for robust 3D reconstruction in partially observed and occluded bin environments. Influence the team's technical strategy and contribute to the long-term vision and roadmap for 3D perception in fulfillment robotics. Partner with cross-functional stakeholders across engineering, science, and operations teams to define requirements, iterate on system design, and deliver end-to-end solutions from research prototype to production deployment. Maintain high standards by participating in design and code reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and validate concepts through simulation, synthetic data evaluation, and live robotic workcell testing using 3D metrics (mIoU, IoU) and affordance-based evaluation frameworks. Mentor applied scientists and engineers, raise the technical bar, and foster a culture of scientific rigor and rapid experimentation. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
Sr. Manager, Applied Science, Sponsored Products and Brands
US, CA, Palo Alto
Are you passionate about using AI to protect and elevate the shopping experience for hundreds of millions of customers? We're looking for a Sr. Applied Science Manager to lead the Sponsored Products and Brands, Search Relevance team, ensuring that every product recommendation shoppers see on our search results page is relevant, trustworthy, and genuinely useful. In this role, you'll be at the forefront of defining what a great search experience looks like by inventing entirely new ways to help customers understand why a product matches their needs. Safeguard the Customer Experience - Lead the development of predictive models that identify and mitigate irrelevant, misleading, or low-quality product recommendations across search placements - Design and scale AI-powered systems whose outputs are used to continually improve the search results - Expand the definition of search quality beyond query-ASIN relevance to encompass shopper preferences, page-level context, and overall experience coherence Define and Measure Search Quality at Scale - Own the development and ongoing measurement of customer experience metrics as reported to senior leadership - Partner with leaders across Search, Retail, and Advertising organizations to align on metric definitions and establish independent measurement standards where needed - Build scalable measurement frameworks that anticipate evolving search presentation formats and proactively adapt to them Invest in State-of-the-Art AI Infrastructure - Drive investment in AI infrastructure and systems that reduce the cost of measuring and monitoring CX metrics across all placements and marketplaces - Build increasingly precise AI models that minimize human effort for auditing, training data generation, and placement-specific customization - Make strategic build-vs-reuse decisions by staying closely connected to peer teams across the organization Invent New Product Experiences - Create novel product presentation experiences that help shoppers understand the differential value of each result — through intelligent selection of images and titles, contextual explanations, and visual indicators such as badges and tags - Partner with peer teams to share learnings and advocate for the adoption of successful innovations beyond your immediate scope Lead and Influence Across the Organization - Manage and grow a team of applied scientists, engineers, and product managers - Partner with senior leaders (Director-level and above) across multiple organizations to drive alignment on shared goals and cross-functional initiatives - Represent search quality and customer experience in executive-level reviews and reporting cadences
Senior Applied Scientist
DE, BE, Berlin
At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. ABOUT THIS ROLE As a Senior Applied Scientist, you will solve large complex real-world problems at scale, draw inspiration from the latest science and technology to empower undefined/untapped business use cases, delve into customer requirements, collaborate with tech and product teams on design, and create production-ready models that span various domains, including Machine Learning (ML), Artificial Intelligence (AI) and Generative AI, Natural Language Processing (NLP), Reinforcement Learning (RL), real-time and distributed systems. ABOUT YOU Your work will focus on inventing and extending scientific approaches, models, and algorithms driven by customer needs at the product level, framing new research problems even when the problem is ill-defined and no textbook solution exists. You will lead the design, implementation, and delivery of scientifically complex, end-to-end solutions that are deployed into production, defining system-level requirements and writing a significant portion of the critical-path code. You will develop reusable science components and services that resolve architecture deficiencies and customers’ pain points, while making technical trade-offs for long-term/short-term. You will work independently with limited guidance, and your decision-making will consistently incorporate robust, data-driven business and technical judgment. You will drive your team’s scientific agenda, author internal or external peer-reviewed publications that validate the novelty of your work, mentor and develop other scientists, and build consensus across multiple teams. You will have the opportunity to innovate, invent, and think big, and influence the experiences of millions of customers. We are looking for a results-oriented Senior Applied Scientist with deep expertise in ML, NLP, Deep Learning, GenAI, and/or large-scale distributed computation. As an Applied Scientist, you will... - Understand complex, ambiguous use cases across the business and adopt/extend/design/invent solutions/models that are scalable, efficient, and automated, where neither the problem nor the solution is well defined - Work closely with fellow scientists and software engineers (at Audible and Amazon) to build and productionize models, and deliver novel and highly impactful features - Review models of peers for the purpose of reducing and managing risk to the business, while improving customer experience - Lead the design, development, and production deployment of scientifically complex, end-to-end solutions for Content Understanding, Recommendations, and GenAI-based product features, defining system-level requirements - Drive and lead initiatives that employ the most recent advances in ML/AI/GenAI, drive your team’s scientific agenda, and author peer-reviewed publications - Mentor and grow scientists on the team and across Amazon, and push the boundary of innovation ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.
2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition
IT, Turin
We're building systems that turn research into real world impact. We're advancing the intelligence behind how customers converse, connect and interact with Alexa across the globe and we're tackling complex conversational AI challenges with state of the art GenAI systems. We're looking for PhD students across multiple research domains to invent, design, and implement state of the art solutions for never before solved problems. Your work here won't just stay in a notebook, it ships to production and reaches customers worldwide. Check out the details below including the job responsibilities, team details, and basic qualifications before submitting your application. You can find more information about the Amazon Science community as well as interview preparation tips via the links below; - https://www.amazon.science/ - https://amazon.jobs/content/en/career-programs/university/science - https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You'll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon's customer focused culture. The ideal intern should have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life You'll spend your first weeks scoping your project with your mentor, then own the research and implementation end-to-end. Projects may include developing and implementing real-time AI services, conducting comprehensive analysis of GenAI algorithms and methodologies or contributing to the creation of reliable, efficient code for production systems that serve millions of users — depending on the team you're matched with. Many interns publish at top-tier conferences or see their work deployed to production before the internship ends. Interns may also be considered for a return offer at the end of their internship, subject to performance evaluation and headcount availability. Further benefits of an Amazon Science internship include; - All of our internships offer a competitive salary - Interns are paired with an experienced manager and mentor(s) - Interns get invited to different intern program or office events - Interns can build their professional and personal network with other Amazon Scientists - Interns can potentially publish work at top tier conferences About the team You'll submit a single application and we'll match you with science teams best aligned with your research interests. Applications are reviewed on a rolling basis, and your application stays active until we find a team match or confirm there are no matches available. Start dates are available throughout the year for durations of between 3–6 months. Please note, each team has different start date and duration preferences — your recruiter will confirm the preferences of the team you're matched with prior to interviewing. We offer science internships in multiple locations across the EMEA region and you can indicate your interest in all these locations by applying here (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, South Africa, Spain, Sweden, UAE, and UK). Please note we do not offer remote internships.
Sr Applied Scientist, FAIM (Full Funnel Agentic Intelligence and Models)
US, NY, New York
Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Applied Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities - Drive the science vision for developing evaluations of the Ads Agent - Partner with senior and principal scientists from across Ads to develop best practices for evaluating agent performance both online and offline - Raise the science bar for all agent evaluations developed in Ads. - Deliver a data driven foundation for making decisions about when new features are ready to launch in Ads Agent.
Applied Scientist, Neuron ARG
US, WA, Seattle
The Automated Reasoning Group in the Amazon Neuron team is looking for an Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch and JAX. Your role will involve working closely with our custom-built Machine Learning accelerator, Trainium, which represents the forefront of innovation for advanced ML capabilities, and is the underpinning of Generative AI. In this role as an Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted AWS Neuron engineer, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, theorem provers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required.
Senior Simulation Scientist, Amazon Logistics
US, WA, Bellevue
Are you inspired by invention? Do you want to see how your work impacts the bigger picture? If yes, you'll fit right in at Amazon's Last Mile Simulations and Analytics Engineering team. The WW AMZL Simulations and Analytics Engineering team drives innovation across our Last Mile network delivery stations through digital twin technology and the seamless integration of process engineering, physics, design, technology, and robotics. We start with the customer and work backwards in everything we do. If you're interested in joining a rapidly growing team building a unique, solutions-advisory group with a relentless focus on the customer, you've come to the right place. This is a blue-sky role where you'll roll up your sleeves and dive into big data sets to build digital twins—discrete event simulations, robotics models, and physics-based 3D simulations using tools like Flexsim, AnyLogic, and Emulate 3D. You'll develop experimentation systems at scale, integrating process workflows, physical dynamics, facility design, emerging technologies, and robotic automation to create comprehensive virtual replicas of our operations. Build optimization algorithms and leverage advanced technologies across Amazon. This is an opportunity to think big about solving challenging problems for our customers. As a Senior Simulation Scientist, you'll deep dive into complex problems and drive relentlessly towards innovative solutions with cross-functional teams. You'll interface and influence various functional teams and individuals at all levels of the organization. Lead strategic digital twin and simulation projects that integrate process design, physics modeling, technology innovation, and robotics to drive operational decisions. Your expertise in synthesizing and communicating insights to audiences of varying technical sophistication will enable you to answer specific business questions and innovate for the future. You'll apply advanced multi-disciplinary methodologies—combining process optimization, physics simulation, design thinking, and robotic systems—to complex use cases across the Last Mile network. You'll also contribute to the end-state vision for digital twin simulation and experimentation of future delivery stations at Amazon. Key job responsibilities • Lead the design, implementation, and delivery of the simulation data science solutions to perform system of systems discrete event simulations for significantly complex operational processes that have a long-term impact on a product, business, or function using FlexSim, Demo 3D, AnyLogic or any other Discrete Event Simulation (DES) software packages • Lead strategic modeling and simulation research projects to drive process design decisions • Be an exemplary practitioner in simulation science discipline to establish best practices and simplify problems to develop discrete event simulations faster with higher standards • Identify and tackle intrinsically hard process flow simulation problems (e.g., highly complex, ambiguous, undefined, with less existing structure, or having significant business risk or potential for significant impact • Deliver artifacts that set the standard in the organization for excellence, from process flow control algorithm design to validation to implementations to technical documents using simulations • Be a pragmatic problem solver by applying judgment and simulation experience to balance cross-organization trade-offs between competing interests and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors for multiple simulation projects • Provide simulation data and measurements that influence the business strategy of an organization. Write effective white papers and artifacts while documenting your approach, simulation outcomes, recommendations, and arguments • Lead and actively participate in reviews of simulation research science solutions. You bring clarity to complexity, probe assumptions, illuminate pitfalls, and foster shared understanding within simulation data science discipline • Pay a significant role in the career development of others, actively mentoring and educating the larger simulation data science community on trends, technologies, and best practices • Use advanced statistical /simulation tools and develop codes (python or another object oriented language) for data analysis , simulation, and developing modeling algorithms • Lead and coordinate simulation efforts between internal teams and outside vendors to develop optimal solutions for the network, including equipment specification, material flow control logic, process design, and site layout • Deliver results according to project schedules and quality A day in the life If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
Applied Scientist II, Amazon Business - External Integrations
IN, TS, Hyderabad
Amazon Business System Integrations connects Amazon Business with millions of business users and thousands of partner procurement, ERP, and finance systems. We build the APIs, protocols, and agentic experiences that let customers buy on Amazon Business directly from the systems they already use. As an Applied Scientist on this team, you will own and advance the ML and GenAI capabilities that make these integrations smarter, faster, and more reliable. You will drive model accuracy, scientific innovation, and global scale across external systems integrations. Our work includes API-based and protocol-based shopping, such as punchout and emerging agent protocols, along with agentic interactions where buyers and AI agents search, compare, and purchase through natural language. It also covers partner onboarding and integration automation, mapping and normalizing catalog, product, and order data across many partner formats, and evaluation frameworks that measure the quality, accuracy, and latency of our agents. Key job responsibilities As an Applied Scientist on this team, you will work across three dimensions: Invent, Implement, and Influence. Invent: You bring deep domain knowledge and fluency with state-of-the-art scientific approaches and emerging research. You practice customer-obsessed science, working backwards from the needs of business buyers, procurement teams, and integration partners to extend or invent new ML approaches, even when no textbook solution exists. You design novel ML and LLM-based methods for agentic shopping, product search and matching, schema and data mapping across partner systems, and automated evaluation of agent quality. You help define the research agenda for our agentic and protocol-based capabilities, and you lead the scientific strategy for accurate, low-latency responses in conversational and API-driven procurement. You author or co-author articles for internal or external peer-reviewed venues when appropriate and not precluded by business considerations. Implement: The ML components you develop are integrated directly into production systems serving Amazon Business customers and partners worldwide. You make sound tradeoffs between accuracy and latency, innovation and stability, and short-term and long-term solutions, favoring reuse and established frameworks where appropriate. You make progress semi-autonomously with only occasional guidance, implement at the right level of complexity the first time, and evaluate emerging technologies, including LLMs and GenAI frameworks, largely on your own. You ensure your models and pipelines integrate robustly with catalog, offer, cart, order, and partner data sources and with the services that power external integrations. Influence: You contribute to planning for the team, including goals, priorities, and roadmaps for ML and GenAI capabilities. You lead the scientific strategy for expanding agentic and integration capabilities across new partners, protocols, and countries. You collaborate with engineering, product, and program teams across Amazon Business so that data integration and ML pipelines are robust and scale with adoption. You mentor scientists and engineers, champion best practices for the AI-Driven Development Life Cycle (AIDLC), and provide peer feedback on research methods and results. You help recruit bar-raising talent through interview drives, and you are visible in the broader scientific community as a subject matter expert, including serving as a Program Committee member or reviewer for peer-reviewed venues.
Senior Applied Scientist, Amazon Business - External Integrations
IN, TS, Hyderabad
Amazon Business System Integrations connects Amazon Business with millions of business users and thousands of partner procurement, ERP, and finance systems. We build the APIs, protocols, and agentic experiences that let customers buy on Amazon Business directly from the systems they already use. As an Applied Scientist on this team, you will own and advance the ML and GenAI capabilities that make these integrations smarter, faster, and more reliable. You will drive model accuracy, scientific innovation, and global scale across external systems integrations. Our work includes API-based and protocol-based shopping, such as punchout and emerging agent protocols, along with agentic interactions where buyers and AI agents search, compare, and purchase through natural language. It also covers partner onboarding and integration automation, mapping and normalizing catalog, product, and order data across many partner formats, and evaluation frameworks that measure the quality, accuracy, and latency of our agents. Key job responsibilities As an Applied Scientist on this team, you will work across three dimensions: Invent, Implement, and Influence. Invent: You bring deep domain knowledge and fluency with state-of-the-art scientific approaches and emerging research. You practice customer-obsessed science, working backwards from the needs of business buyers, procurement teams, and integration partners to extend or invent new ML approaches, even when no textbook solution exists. You design novel ML and LLM-based methods for agentic shopping, product search and matching, schema and data mapping across partner systems, and automated evaluation of agent quality. You help define the research agenda for our agentic and protocol-based capabilities, and you lead the scientific strategy for accurate, low-latency responses in conversational and API-driven procurement. You author or co-author articles for internal or external peer-reviewed venues when appropriate and not precluded by business considerations. Implement: The ML components you develop are integrated directly into production systems serving Amazon Business customers and partners worldwide. You make sound tradeoffs between accuracy and latency, innovation and stability, and short-term and long-term solutions, favoring reuse and established frameworks where appropriate. You make progress semi-autonomously with only occasional guidance, implement at the right level of complexity the first time, and evaluate emerging technologies, including LLMs and GenAI frameworks, largely on your own. You ensure your models and pipelines integrate robustly with catalog, offer, cart, order, and partner data sources and with the services that power external integrations. Influence: You contribute to planning for the team, including goals, priorities, and roadmaps for ML and GenAI capabilities. You lead the scientific strategy for expanding agentic and integration capabilities across new partners, protocols, and countries. You collaborate with engineering, product, and program teams across Amazon Business so that data integration and ML pipelines are robust and scale with adoption. You mentor scientists and engineers, champion best practices for the AI-Driven Development Life Cycle (AIDLC), and provide peer feedback on research methods and results. You help recruit bar-raising talent through interview drives, and you are visible in the broader scientific community as a subject matter expert, including serving as a Program Committee member or reviewer for peer-reviewed venues.
Manager III, Data Science - AMZ14164.12
US, VA, Herndon
Employer: Amazon Data Services, Inc. Position: Manager III, Data Science - AMZ14164.12 Location: Herndon, VA Multiple Positions Available: 1. Lead a team of data scientists in developing and deploying machine learning models for infrastructure capacity planning and demand forecasting at global scale. 2. Design predictive models and statistical frameworks to optimize resource utilization and inform long-term infrastructure investment decisions. 3. Develop risk-based planning approaches utilizing advanced statistical methods and ML techniques to quantify uncertainty and set operational targets. 4. Build and maintain end-to-end ML pipelines for model training, inference, and monitoring in production environments. 5. Collaborate with engineering, product, and finance partners to translate complex business requirements into quantifiable metrics and automated decisionsupport systems. 6. Design experimentation frameworks and model validation methodologies to ensure accuracy and reliability of forecasts. 7. Lead weekly business reviews of model performance, presenting findings and recommendations to senior leadership. 8. Manage and mentor a team of data scientists and engineers across multiple levels, driving best practices in ML development, code quality, and production deployment. 9. Utilize Python, SQL, and cloud-based ML infrastructure to deliver scalable data science solutions. (40 hours / week, 8:00am-5:00pm, Salary Range $175100 - $236900) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
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