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AI for Information Security call for proposals — Fall 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 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:

  • Threat, intrusion, and anomaly detection for cloud security
  • Generative AI and foundation models for information security
  • Graph modeling and anomaly detection on graphs
  • Learning with limited/noisy labels and weakly supervised learning
  • ML for malware analysis and detection, with a focus on cloud environments or devices
  • Finding security vulnerabilities using ML
  • Causal inference for information security
  • Zero/one-shot learning for information security
  • Reinforcement learning for information security
  • Protecting and preserving data privacy in the cloud
  • Securing generative AI and foundation models

Timeline

Submission period: September 25, 2024 - November 13, 2024 (11:59PM Pacific Time)

Decision letters will be sent out in March 2025

Award details

Selected Principal Investigators (PIs) may receive the following:

  • Unrestricted funds, no more than $80,000 USD on average
  • 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 Rules and Eligibility 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.

How to apply
When you're ready to submit your proposal, use the button below and follow the instructions on the site.
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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. 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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.
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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.
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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.
Senior Applied Scientist, Computer Vision and Robotics, Amazon Robotics
US, MA, North Reading
Come shape the future of robotic sensing hardware by building the computer vision and ML systems that define how every robot in Amazon's global fulfillment network sees, senses, and understands the world. This is applied science embedded in a hardware development team where you directly shape what gets built, manufactured, and deployed at scale. As a Senior Applied Scientist on the Amazon Robotics Sensing and Compute hardware team, you will be the ML and perception expert working alongside the Principal Applied Scientist and Principal Hardware Development Engineer to shape the future of deployed and scaled sensing hardware. You will influence sensor selection, compute architecture, optical design, and system level trade offs, ensuring the hardware we build is purpose designed for the algorithms that run on it. You will help define what the next generation of cameras, depth sensors, and edge compute modules need to be, and then prove it with data, experimentation, and production validation. What makes this role unique: -Embedded in the hardware development team, directly influencing what gets designed, manufactured, and shipped -Partner with the Principal Engineers to co define the sensing and compute hardware roadmap for every major Amazon Robotics program -Own the ML experimentation and validation that drives hardware architecture decisions for cameras, depth sensors, and edge compute -Solve perception problems no other company is tackling: safety rated vision, multi modal sensor fusion, and real time inference on resource constrained hardware -Ship to hundreds of fulfillment sites worldwide across autonomous mobile robots, manipulation workcells, barcode sensing, and safety rated human collaborative systems -Your work does not sit in a research queue. It goes into production hardware that enables the future of Amazon Robotics Key job responsibilities -Partner with the Principal Hardware Development Engineer and Principal Applied Scientist to co define sensing and compute hardware requirements, ensuring ML and perception needs drive sensor selection, optical design, compute architecture, and system level trade offs -Develop and optimize computer vision and deep learning models for robotic perception including object detection, depth estimation, scene understanding, and barcode recognition, purpose built for edge deployment on resource constrained hardware -Lead ML experimentation and validation that directly informs hardware architecture decisions for cameras, depth sensors, and edge compute modules -Design and execute benchmarking frameworks to evaluate sensing hardware performance across diverse robotic use cases, providing data driven recommendations on what to build next -Optimize models for production edge deployment including quantization, pruning, distillation, and inference pipeline tuning to meet strict latency, power, and accuracy requirements -Drive research and experimentation on next generation perception capabilities in collaboration with the Principal Applied Scientist, including multi modal sensor fusion, safety rated vision, and real time inference for autonomous and human collaborative robotics -Collaborate cross functionally with hardware engineers, software developers, and robotics program teams to transition models from prototype through production validation and fleet deployment -Contribute to the sensing and compute hardware roadmap alongside the Principal Applied Scientist, identifying where ML trends and emerging capabilities require new hardware architecture or sensor modalities -Mentor engineers and scientists on ML best practices for embedded systems and production robotics -Publish and present research to advance the state of the art in robotic perception A day in the life In this role you will work at the intersection of machine learning and physical robotics hardware. You will collaborate with hardware engineers to evaluate how your models perform on real sensors and edge compute, run experiments that directly influence what gets built next, and validate perception systems alongside robotics program teams. You will push the boundaries of computer vision for robotic applications while seeing your work deploy on real robots in real fulfillment centers. This is applied science with immediate, measurable impact at a scale no other robotics company can offer. 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! About the team The Amazon Robotics Sensing and Compute team powers a customer centric ecosystem built for speed, quality, and innovation. We stay ahead of sensing and compute technology, standardize reusable and cost effective modules, and pair them with deep engineering expertise and a robust supply chain. Every customer gets the right solution, whether off the shelf, rapidly customized, or purpose built, easy to integrate and ready to scale. We are a small, fast moving team of builders who own the full stack from optics through edge compute and take pride in shipping production hardware that enables every major robotics program in Amazon's global fulfillment network. If you want your work to matter on day one, this is the team.
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.
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