AI for Information Security call for proposals — Fall 2024

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

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.

When you're ready to submit your proposal, use the button below and follow the instructions on the site.

Related content

US, WA, Redmond
The Head of Flight Dynamics is the single-threaded leader responsible for orbital maneuvering, orbital safety, and orbital performance across Amazon Leo's current and future satellite constellations. This role directly impacts service reliability for millions of customers worldwide through safe, precise on-orbit operations and industry-leading space safety practices. You will lead an organization of engineers, research scientists, and software developers building the algorithms, end-to-end ground system products, analysis tools, and infrastructure that enable safe and precise operation of Leo satellites at constellation scale. You will also lead special Flight Dynamics programs including GNSS independence, space traffic coordination services, and space situational awareness. The ideal candidate thrives in ambiguity, operates autonomously while building alignment across organizations, and has a proven track record of delivering complex, cross-organizational technical programs that solve critical technical and business challenges. You will partner directly with engineering leaders, product managers, legal and regulatory teams, and senior Leo leadership to drive technical clarity, architect solutions, make strategic trade-offs, and deliver these enabling capabilities for Amazon Leo. You will embody Amazon's Leadership Principles — particularly Ownership, Dive Deep, Are Right A Lot, and Hire and Develop the Best. 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 Flight Dynamics roadmap and technical strategy, including forward-looking initiatives that enable Amazon Leo to deliver a best-in-class customer experience and leadership in orbital safety. - Build, lead, and develop a high-performing organization of engineering managers and senior individual contributors; hire and grow top talent and foster a culture of innovation, operational excellence, and customer obsession. - Own and lead the architecture, development, and deployment of resilient ground software services and infrastructure that manage safe orbital maneuver operations of Amazon Leo satellites — ascent, station keeping, collision avoidance, and end of life descent for re-entry - Own and lead the development of algorithms for orbit determination, trajectory planning and guidance, and high-fidelity orbit modeling and prediction. - Collaborate closely with GN&C and Mission Operations to enable safe, autonomous on-orbit satellite operations that integrate seamlessly with Flight Dynamics ground systems. - Deliver the products needed by stakeholders for spacecraft design, constellation planning, and operations — including launch targets and launch-window assessments for launch and mission management teams - Provide leadership oversight and direction to the GNSS independence and space situational awareness programs. - Lead the development of metrics, monitoring, and response systems that leverage physical modeling and ML/AI to automate analysis of data from thousands of satellites for closed-loop assessment and improvement of on-orbit performance. - Initiate and lead operator-to-operator collaboration for orbital safety, and partner with regulatory and legal teams to promote adoption of best practices in space safety across the industry. - Own Flight Dynamics system risks and drive mitigations to minimize impact; manage program schedules and drive on-time delivery with support from technical program managers, balancing operational support against development efforts. - As a key voice in on-orbit operations, work with Mission Operations and peer leaders to influence efficiency, safety, performance, and simplicity for customers.
IN, KA, Bengaluru
Amazon Pay strives to be Earth’s most customer-centric payments service. Our mission is to serve customers and merchant partners with the most trusted, friction-less and rewarding payment solutions for their needs on and off Amazon. We are seeking an exceptional Data Scientist III to drive innovation in machine learning and artificial intelligence solutions while leading high-impact initiatives across the organization. Key job responsibilities Technical Excellence Lead end-to-end machine learning projects using PyTorch, AWS SageMaker, and other leading ML frameworks Design and implement complex statistical models and deep learning solutions Develop and optimize MLOps pipelines for model training, evaluation, and deployment Experience with modern LLM frameworks and Generative AI applications Expertise in Python, R, and related data science libraries MLOps & Development Build automated ML pipelines using AWS services (CodePipeline, Lambda, Step Functions) Implement CI/CD practices for ML model deployment and monitoring Create containerized solutions using Docker for scalable model deployment Experience with model optimization and hyperparameter tuning using tools like Optuna Integrate ML solutions with monitoring tools like MLflow Business Impact & Leadership Partner with stakeholders to translate business problems into technical solutions Design and develop business intelligence applications for real-time insights Lead technical initiatives and mentor junior data scientists Drive cross-functional collaboration to deliver innovative solutions Communicate complex technical concepts to non-technical audiences About the team The Amazon Pay Data Products team is a central unit that builds and maintains data products supporting Amazon Pay's growth across multiple markets. We operate at scale, processing 150M+ monthly transactions and managing 12 PB of data infrastructure. Our team consists of Business Intelligence Engineers, Data Engineers, and Product Managers who develop and maintain standardized reporting, data marts, and self-service analytics tools. Our expanded capabilities cover data science and Gen AI wherein we have built our first suite of multi-agent systems.
US, WA, Seattle
AI assistants are getting genuinely good at remembering individuals: your preferences, your projects, the thread you left open last week. But that memory stops at the edge of one person's usage. It doesn't reach the level at which real work happens, where the knowledge that matters is spread across many people, where one person's decision changes what everyone else should do next, and where nobody has the full picture. We're building AI that operates at that level: a durable, accurate understanding of how a team works, used to make that team measurably faster. We are looking for a Principal Applied Scientist to own the scientific direction of that work. This is a broad, ambiguous, high-leverage charter. The problems span knowledge representation, temporal reasoning, retrieval, agentic behavior, and the measurement science needed to know whether any of it is working. You will not be handed a well-posed problem. You will decide which problems are worth posing. This is a science leadership role, not a solo research role. You will set direction and raise the scientific bar across a team of applied scientists and MLEs, while staying deep enough in the work to prototype an idea yourself and prove it on real data. Key job responsibilities Own the scientific strategy for how organizational knowledge is represented, kept current, and retrieved: extraction, entity resolution, deduplication, graph structure, and retrieval that unifies graph, semantic, keyword, and temporal search. Advance temporal reasoning. Knowledge changes: facts are revised, decisions are reversed, priorities move. Representing what superseded what and when, and preserving the provenance to distinguish confirmed information from inferred information, is among the hardest open problems in this space. Define the science of proactive behavior. When is it right for an AI system to interrupt a human? These are precision-critical problems where a false positive costs far more than a miss, and where the right threshold varies by team and by individual. Lead our measurement science. Build evaluation for completeness and correctness across a multi-component agentic system, converging on a small number of trustworthy primary metrics rather than a sprawl of component scores. Judge honestly when an offline gain is real and when it is an artifact of a sparse dataset. Build the data that doesn't exist. The most valuable phenomena in this domain are also the rarest, which makes naturally occurring examples too scarce to learn from. Design synthetic and simulated data pipelines that generate controlled, realistic scenarios so these capabilities can be developed and tested at all. Own the learning loop. Turn human interaction into usable training signal, and set the direction for how the system improves from explicit feedback in the near term and from passive observation over the longer term. Make the efficiency calls. Decide where frontier models are required and where a smaller domain-tuned model is sufficient, and build the cost and capacity measurement that makes it a data-driven decision rather than an opinion. Raise the bar across the team. Mentor scientists, review designs, publish where the work merits it, and represent the science externally to customers and to the research community. A day in the life You might spend the morning in a design review arguing that a proposed approach won't survive contact with real data, the afternoon writing a prototype yourself to demonstrate the alternative, and the end of the day convincing an engineer that the capability is worth a sprint. Our sequencing is deliberate: try the idea on intuition, validate it on real data by inspection, then measure it, then operationalize it. Scientists here are expected to identify a problem, justify it, recruit others to it, and drive it into production, across whatever parts of the system that requires. Ownership follows the problem, not the org chart. About the team We are a combined science, product, and engineering team building one product together. Scientists own capabilities end to end rather than individual components, because these problems don't decompose cleanly: a single improvement typically touches extraction, storage, and retrieval at once. We invest in the tooling that makes that practical: local full-stack environments and sandboxed realistic data, so a scientist can go from idea to result in seconds rather than waiting on a deployment or on engineering support. The work is grounded in real usage rather than benchmarks alone, which is a rare combination for science this early: real users, real data, real feedback, and a genuinely unsolved research agenda.
US, WA, Seattle
This role sits within Amazon's Automated Reasoning and Formal Verification research horizon. Shape the Future of Cloud Computing. Are you a graduate student passionate about Automated Reasoning and its real-world applications? Join our team of innovators and embark on a journey to revolutionize cloud computing through innovative automated reasoning techniques. Our tools are called billions of times daily, powering the backbone of Amazon's products and services. We are changing the way computer systems are developed and operated, raising the bar for security, durability, availability, and quality. Applied Scientists in Automated Reasoning develop and apply formal methods, automated reasoning techniques, and neurosymbolic approaches to ensure the security, reliability, and correctness of Amazon and AWS services and customer applications. Application areas span cloud infrastructure verification, cryptographic assurance, AI safety, and formal guarantees for generative AI systems. Methods range from interactive theorem proving and constraint solving to neuro-inspired proof search. As an Applied Science Intern, you will have the opportunity to work alongside our scientists and contribute to projects. From distributed proof search and SAT/SMT solvers to program analysis, synthesis, and verification, you will tackle complex challenges at the intersection of theory and practice. Amazon has positions available for Automated Reasoning Applied Science Internships in, but not limited to, Arlington, VA; Boston, MA; New York, NY; Portland, OR; Santa Clara, CA; Seattle, WA; Austin, TX; Cambridge, UK. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis. Contribute to the design and implementation of algorithms and formal methods for automated reasoning, including constraint solving, model checking, static analysis, theorem proving, and program synthesis, within a guided research framework. Explore and apply generative AI and machine learning techniques to enhance automated reasoning, including learning-based heuristics for search, neural approaches to symbolic reasoning, and methods for verifying the correctness of AI-generated code. Contribute to automated reasoning techniques for generative AI and agentic coding systems, including methods that apply formal guarantees to large language model outputs. Contribute to the scientific community through publications at peer-reviewed conferences and journals. Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. Critically review and validate outputs from AI tools and automated systems. The ideal intern must have the ability to communicate research findings clearly to diverse audiences.
US, WA, Bellevue
Amazon's Modeling and Optimization (MOP) team seeks motivated individual with strong analytical and algorithmic skills to optimize the global logistics network and its operations. Key job responsibilities - Enhance global logistics network efficiency through data-driven optimization - Reduce variable costs by improving network design, inventory placement, process and operational planning, and resource allocation - Optimize capital investment through strategic fixed asset deployment planning - Develop metrics to quantify business impact of implemented solutions A day in the life - Lead development of production-ready algorithms and scientific tools for under-the-roof (UTR) and network process analysis and optimization - Drive planning and execution decisions on operation timing and resource allocation to improve capacity, cost, and speed. - Manage customer interactions, promote science-based processes, and incorporate customer needs into tool improvements. - Partner with team members and customers to exercise judgment on appropriate analysis methods for various business requests. - Interact with and influence adjacent systems and tools, including those for long-term operating policies and daily capacity planning. - Blend scientific expertise with business acumen to deliver impactful solutions across the organization.
US, WA, Seattle
Our team in Amazon Robotics builds robotic systems that perform contact-rich manipulation tasks safely and reliably in complex, unstructured environments — at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an Applied Scientist to join our Motion Behaviors team. You will drive the development of learned controllers and manipulation behaviors, applying techniques like reinforcement learning and behavior cloning to robots operating in Amazon fulfillment centers. These problems remain unsolved at scale: our robots must improve continuously in environments where simulation alone is insufficient. You will make principled decisions about when learned approaches should replace engineered solutions, and how to select behaviors based on estimated risk. You will collaborate across disciplines and leverage rich operational data to continuously improve system performance. Key job responsibilities • Develop learned controllers and manipulation behaviors, from research prototyping through deployment on production robots. • Research, design, and implement motion planning, control, and decision-making algorithms that improve the performance of deployed systems. • Design and deploy learning pipelines that take policies from simulation training to reliable, real-time execution on physical robots. • Develop models that predict manipulation outcomes and inform behavior selection under uncertainty. • Leverage operational data from deployed systems to systematically identify failure modes and drive policy improvements. • Represent Amazon in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that assist 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!
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for passionate, hard-working, and talented individuals to help us push the envelope of content localization. We are seeking scientists with experience in audio processing, speech/voice AI and machine learning. We work on a broad array of research areas and applications, including but not limited to multimodal machine translation, speech synthesis, speech analysis, and asset quality assessment. Candidates should be prepared to help drive innovation in one or more areas of machine learning, audio processing, and natural language understanding. If you have experience with speech synthesis and foundational models, then that's a huge plus! Key job responsibilities As an Applied Scientist, you should be a strong communicator, able to describe scientifically rigorous work to business stakeholders of varying levels of technical sophistication. You will closely partner with the solution development teams, and should be intensely curious about how the research is moving the needle for business. Strong inter-personal and mentoring skills to develop applied science talent in the team is another important requirement. - Lead research and development of speech and audio generation technology and end-to-end speech-to-speech architecture - Develop audio processing solutions for production environments, including source separation, enhancement, and mixing - Define the research roadmap for your area, identify high-impact problems, and communicate technical direction to senior leadership - Publish research, contribute to the broader scientific community, and bring external advances into production systems A day in the life You might start your morning reviewing experimental results and refining a model architecture before syncing with your engineering partners on integration plans. After lunch, you could be whiteboarding a new approach to a problem your team recently identified, then writing up findings for an internal science review. You will regularly present your work to peers and stakeholders, participate in code and design reviews, and explore emerging research that could unlock new possibilities for your team. About the team Our team is driven by a shared commitment to applying science in ways that create meaningful impact for customers. We value rigorous research, collaborative problem-solving, and a willingness to experiment with new ideas. You will work alongside talented scientists and engineers in an inclusive environment where your contributions shape the direction of our work. We are focused on building solutions that matter at scale, and we are looking for teammates who are energized by that challenge.
US, NY, New York
We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.
US, WA, Seattle
We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. Equally important is deep expertise in causal machine learning — including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) — to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale. Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization. Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems. Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements. Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: * shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. * Error detection and price quality guardrails at scale. * Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team The Pricing Optimization science group builds and refines Amazon's algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience. The team also brings hands-on experience with causal modeling and inference — including uplift modeling and treatment effect estimation — to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment.
IN, KA, Bangalore
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed, Sr. Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes. This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences
US, CA, Palo Alto
About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities As a Machine Learning Applied Scientist, you will: * Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities * Develop scalable and effective machine-learning models and optimization strategies to solve business problems * Run regular A/B experiments, gather data, and perform statistical analysis * Work closely with software engineers to deliver end-to-end solutions into production * Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving * Conduct research on new machine-learning modeling and Generative AI solutions to optimize all aspects of Sponsored Products and Brands business About the team The Ad Response Prediction team within Sponsored Products and Brands (SPB) drives personalized shopping experiences for SPB Ads across placements, pages, and devices worldwide. We achieve this through ML and GenAI solutions that include customized shopper response prediction and session-level understanding to optimize every stage of the ad-serving process, from sourcing and bidding to widget discovery and auctions. Our responsibilities include advancing response prediction through model and feature innovations and extending prediction beyond the auction stage to areas such as targeting, sourcing, and bidding.
US, NY, New York
We are seeking a Measurement and Attribution Analytics scientist to further the development and application of analytics methods to examine the complex data flows of measurement, attribution and shopper analytics and to translate deep-dives into actionable insights for our product teams. In this role you will develop new tools to analyze our advertising data to help improve the performance of our bidding algorithms, targeting and relevance systems, help advance our supply strategy, and evaluate the adoption and impact of feature releases. Key job responsibilities - Analyze data trends regarding supply, optimization, ad load, and advertising mix effects that affect advertiser performance and contribute to achieving advertiser goals. - Present papers to senior leaders on issues like feature development impact on identity recognition rates, and changes of ad selection systems to improve fill rate highlighting insights that will inform our business development and engineering roadmaps. - Identify, standardize, and operationalize KPIs to effectively measure the performance of all systems involved in ad serving, and use trend insights to inform business priorities. - Partner with engineering teams to define data logging requirements and getting these prioritized in engineering roadmaps. - Validate financial models through analysis - Develop and own ad revenue and supply intelligence analytics decks that provide ongoing deep-dives A day in the life The Measurement and Attribution Scientist will work closely with business leaders and engineers on developing common data architecture that will optimize our data logging at different grains, and will allow data interoperability from bid flow to optimization to campaign delivery. There will be an emphasis on understanding the impact of shopper journeys to and from Amazon properties and their implication on product development. The candidate will then analyze the data and present papers and ongoing reports on actionable insights. About the team The Ads Science Product Team's Mission: Work alongside those who need product data to apply objective perspective and business logic to uncover insights, advise strategic decisions, and adjust to industry changes.
IN, KA, Bangalore
Does the thought of improving one of the world’s most complex logistic systems inspire you? Is your passion to sift through hundreds of systems, processes, and data sources to solve the puzzle and identify the next big opportunity? Are you a creative big thinker who is passionate about using data to direct decision making and solve complex and large-scale challenges? Are you fascinated by the interactions between operations and strategy? Do you feel like your skills uniquely qualify you to bridge communication between teams with competing priorities? If so, then this position is for you! Come help Amazon create state-of-the-art science-driven technologies for delivering packages to the doorstep of our customers! The Last Mile Routing & Planning organization builds the software, algorithms and tools that make the “magic” of home delivery happen: our flow, sort, dispatch and routing intelligence systems are responsible for the billions of daily decisions needed to plan and execute safe, efficient and frustration-free routes for drivers around the world. Our team supports deliveries (and pickups!) for Amazon Logistics, Same Day, Amazon Grocery, Lockers, and other new initiatives across the world. Key job responsibilities In this role, your main focus will be to apply algorithms, synthesize information, identify business opportunities, provide data-driven insights and communicate business and technical requirements within the team and across stakeholder groups. You will partner closely with other scientists and engineers in a collegial environment with a clear path to business impact. We have an exciting portfolio of research areas including vehicle routing, planning for electric and autonomous vehicles, district and stops planning, ultra-fast deliveries, fleet planning, and forecasting solutions for different delivery programs leveraging the latest OR, ML, and Generative AI methods, at a global scale. Successful candidates will have a deep knowledge of Operations Research and/or Machine/Deep Learning methods, experience in applying these methods to large-scale business problems, the ability to map models into production-worthy code in Python or Java, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the excitement to take iterative approaches to tackle big research challenges.
US, WA, Seattle
As part of the AWS Applied AI Solutions organization, we have a vision to provide end user applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are easy to adopt and easy to use. The Team Join the next science revolution at AWS Life Sciences Applied AI Solutions, where you'll work alongside world-class scientists to build AI that transforms how therapeutics are discovered, developed, and brought to patients. We're out to revolutionize how medicines are discovered, developed, and brought to patients, powered by a new generation of AI. Our team tackles some of the hardest open problems at the intersection of frontier AI and life sciences. We apply biological foundation models, large language models, and agentic reasoning systems to life sciences problems, then put them into the hands of customers as applications and managed services they can fine-tune, tailor, and deploy on their own data. The science challenges are deep: how do you design agentic systems that reason correctly over complex biological, regulatory, and clinical logic? How do you enable customers to tailor foundation models to their proprietary data and get better outputs with less effort? How do you adapt models to reason faithfully in high-stakes scientific and regulatory domains? Today we're focused on two areas. In clinical trials, we're building AI that automates and optimizes regulatory and clinical development workflows. In drug design, our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. We combine frontier research with production-scale delivery to put breakthrough science into the hands of customers solving humanity's hardest problems. We value scientific rigor, encourage publication, and support conference participation. If you want to do research that ships, this is the team. The Role We are seeking an Applied Scientist to build the models and methods behind our life sciences AI products, with a primary focus on clinical trial operations and agentic reasoning. You will design, train, and evaluate systems that reason over complex clinical and operational logic, and ship them into products customers use directly. You will work closely with senior and principal scientists on well-scoped research problems, own your results end to end, and see your work reach production. This role combines expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will work on how large language models reason, plan, and act in complex scientific domains, while applying domain knowledge to ensure models produce scientifically valid outputs. The problems span multiple fronts: - How do you build LLM-based agentic systems that correctly reason over clinical protocols, regulatory standards, and complex multi-step operational workflows? - How do you evaluate agent reliability and faithfulness rigorously enough to trust in high-stakes clinical settings? - How do you develop model customization methods (fine-tuning, retrieval augmentation, domain adaptation) that let customers get strong results from foundation models on their own data? You will focus on clinical trial operations (agentic automation, structured reasoning, evaluation, domain adaptation), with opportunities to contribute across drug discovery (protein engineering, antibody design) as the portfolio grows. You will own end-to-end scientific solutions from research through production, and your work will directly shape the tools that scientists use daily. Key job responsibilities - Design, train, fine-tune, and evaluate LLM-based agentic systems that reason over clinical protocols, regulatory standards, and operational workflows - Build rigorous evaluation harnesses and benchmarks to measure agent reliability, faithfulness, and failure modes in high-stakes domains - Develop model customization methods (fine-tuning, RLHF, retrieval augmentation, domain adaptation) that help customers get better outputs on their own data with less effort - Contribute to graph-based and causal modeling approaches for clinical trial operations - Partner with Life Sciences domain experts, product, and engineering to translate scientific challenges into shipped capabilities - Own experiments end to end: problem framing, implementation, evaluation, iteration, and hand-off to production - Publish at top-tier venues where the work supports it - Contribute to drug discovery efforts (protein engineering, antibody design) as opportunities arise A day in the life - Design and run an experiment to validate a new agentic reasoning or fine-tuning method, then ship it as a capability customers can use - Diagnose why a model is failing on a new class of inputs and implement a fix to unblock a delivery milestone - Build or extend an evaluation benchmark to measure how faithfully an agent reasons over clinical logic - Meet with domain experts to scope what the next model release needs to do - Review results with a senior scientist, sharpen the approach, and get it over the finish line - Prototype a new idea that could become the next capability in the product About the team AWS Solutions As part of the AWS solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. we blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! This position involves research into and development of advanced DL models for video assets in order to improve their visual quality to be appealing to our customers (e.g. restoration from various defects, improving resolution, restoring truer colors, SDR->HDR up-conversion, better dithering/noising techniques to withstand compression artifacts, etc.). You will also develop next-generation methods for content-adaptive video encoding. You will work closely with subject matter experts (Research Scientists) with depth in the video encoding/quality/processing areas to maximize the video quality that Prime Video customers experience under a given network condition. Your work would directly influence the customer experience making Prime Video the destination for consuming content at the highest quality. Key job responsibilities The work would involve working with creation of ground-truth datasets, creating new video diffusion model architectures and training methodologies that ensure the best temporal consistency, preservation of creative intent, and perceptual quality. You will also work on optimization and deployment of such models in production workflow and will define/measure suitable at-scale success criteria. Aspects of work can involve scene understanding at a semantic level using latest techniques. You would develop innovative solutions that achieve the best balance between speed vs performance, file invention disclosures, and publish these novel approaches in tier-1 conferences. You would collaborate with research scientists with domain depth in video encoding/compression. You would conduct subjective ratings tests to generate ground truth data for your models, when required. You would also contribute to development of new video quality models that correlate highly with subjective ratings and help quantify the improvements brought about by your work. You would define and refine team processes around ML/DL model development, deployment, and guardrail definition. A day in the life You will work with Video Research Scientists who will bring domain expertise to jointly define the specific problem and technical approach. You may collaborate with academia through sponsored research programs on complex problems of long-term importance in your area of work. You will interface with ML engineers to convert your output into production workflows. You will help develop new AI workflows that are more efficient from a compute perspective. You will work with product managers to develop product roadmap that align with business goals. You will help hire new talent and will help develop talent through knowledge sharing and mentorship.
US, WA, Bellevue
As an Applied Scientist in Amazon Fullfilment Technology, you will lead the development of agentic systems to assist with operational decision making and orchestration. You will work building full agentic systems leveraging multi-agent orchestration, tool use, memory, and action execution. You will train LLMs using a combination of rejection sampling approaches, SFT, continual post-training, and Reinforcement Learning (RL). These systems are deployed to Amazon buildings, and you will also work on rigorous offline and online evaluations. Your work will leverage the latest LLMs to develop capabilities for agentic reasoning, coding and analytics. You will also lead research projects to tackle unsolved problems, mentor interns, and author academic papers to summarize your findings for external publication. Key job responsibilities - Generating training and preference data for specific use cases (reasoning trajectories, tool traces) - Reward modeling and policy optimization for LLMs: DPO, IPO, RLHF/RLAIF with PPO/GRPO, rejection sampling. - Supervised fine-tuning on step-by-step trajectories and tool-use traces - Verbal Reinforcement Learning and Continual Learning - RL for LLMs, Offline RL and off-policy evaluation - Agentic memory/state management; episodic and semantic memory; vector search; grounding with RAG. - Evaluation: developing decision quality metrics, scaling LLM-based evaluations. About the team Amazon Fulfillment Technologies (AFT) powers Amazon's global fulfillment network. We invent and deliver software, hardware, and data science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. Learn more about AFT: https://tinyurl.com/AFTOverview
US, WA, Bellevue
As an Applied Scientist in Amazon Fullfilment Technology, you will lead the development of agentic systems to assist with operational decision making and orchestration. You will work building full agentic systems leveraging multi-agent orchestration, tool use, memory, and action execution. You will train LLMs using a combination of rejection sampling approaches, SFT, continual post-training, and Reinforcement Learning (RL). These systems are deployed to Amazon buildings, and you will also work on rigorous offline and online evaluations. Your work will leverage the latest LLMs to develop capabilities for agentic reasoning, coding and analytics. You will also lead research projects to tackle unsolved problems, mentor interns, and author academic papers to summarize your findings for external publication. Key job responsibilities - Generating training and preference data for specific use cases (reasoning trajectories, tool traces) - Reward modeling and policy optimization for LLMs: DPO, IPO, RLHF/RLAIF with PPO/GRPO, rejection sampling. - Supervised fine-tuning on step-by-step trajectories and tool-use traces - Verbal Reinforcement Learning and Continual Learning - RL for LLMs, Offline RL and off-policy evaluation - Agentic memory/state management; episodic and semantic memory; vector search; grounding with RAG. - Evaluation: developing decision quality metrics, scaling LLM-based evaluations. About the team Amazon Fulfillment Technologies (AFT) powers Amazon's global fulfillment network. We invent and deliver software, hardware, and data science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. Learn more about AFT: https://tinyurl.com/AFTOverview
US, WA, Seattle
About Sponsored Products and Brands The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About our team The General Shopping Intelligence (GSI) team is a highly motivated, collaborative, and fun-loving group with a strong entrepreneurial spirit and bias for action. We provide advanced real-time machine learning services that connect shoppers with the right ads across all platforms and surfaces worldwide. Through deep understanding of both shoppers and products, we help shoppers discover new products they love, enable advertisers to reach their customers most efficiently, and help Amazon continuously innovate on behalf of all customers. We are seeking a motivated Applied Scientist who loves to innovate at the intersection of customer experience, deep learning, generative AI and high-scale machine learning systems. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities As an Applied Scientist, you will: * Leverage Generative AI and Large Language Models (LLMs) to mine complex behavioral data, deriving deep, actionable shopper insights that identify customer experience gaps and unlock new business opportunities. * Design and develop scalable machine learning and GenAI models focused on shopper intent and preference modeling, ensuring a rapid path from prototype to production. * Partner closely with engineering teams to architect and deploy end-to-end GenAI solutions into production, integrating advanced insights directly into real-time, customer-facing systems. * Drive the scalability, efficiency, and automation of large-scale model training and real-time inference systems, pioneering the LLM infrastructure required to support next-generation GenAI workloads at Amazon Ads scale. * Design and run rigorous A/B experiments to quantify the business and customer impact of GenAI-driven shopper insights, performing advanced statistical analysis to guide iterative production rollouts. * Conduct applied research in novel generative AI techniques (e.g., fine-tuning, RAG, agentic workflows) to optimize the shopper experience and drive performance across all aspects of the Sponsored Products and Brands business.
US, VA, Arlington
Amazon Web Services (AWS) is the world leader in providing a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world! Passionate about building, owning and operating massively scalable systems? Want to make a billion-dollar impact? If so, we have an exciting opportunity for you. The AWS Managed Operations (MO) organization was founded in April 2023, with the objective to reduce operational load and toil through long-term engineering projects. MO is building the best-in-class engineering and operations team that will own the day-to-day operations for AWS Regions, improving the availability, reliability, latency, performance and efficiency to operate AWS regions. The AWS Managed Operations Intelligence (MOI) Team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insight strategy for AWS. You will be expected to serve as a Full Stack Data Scientist. You will be responsible for driving data-driven transformation across the organization. In this role, you will be responsible for the end-to-end data science lifecycle, from data exploration, ETL, model development and data visualization. You will leverage a broad set of tools and technologies, including general analytical frameworks (Spark, Airflow, etc.), AI frameworks (Hugging Face, etc.) and various machine learning frameworks, to tackle complex business problems. Your analytics research will provide direction on the technology strategy of the Managed Operations organization. Your Decision Science artifacts will provide insights that inform AWS' Operations and Site Reliability Engineering teams. You will work on ambiguous and complex business and research science problems at scale, and you are comfortable working with cross-functional teams and systems. By working together on behalf of our customers, we are building the future one innovative product, service, and idea at a time. Are you ready to embrace the challenge? Come build the future with us. Key job responsibilities - Work with large and complex data sets to solve a wide array of challenging problems using different analytical approaches - Develop ML/AI models. Partner with software teams to productionalize these models. - Data Pipeline and Infrastructure: design and implementation of data pipelines - Metric Development and Monitoring: Define and develop advanced, customized metrics and key performance indicators (KPIs) that capture the nuances of the organization's strategic objectives and operational complexities. Continuously monitor and evaluate the performance of metrics A day in the life Why AWS? Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. About AWS Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. AWS Infrastructure Services (AIS) AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we're the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we're looking for talented people who want to help. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events create stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. About the team The Managed Operations Intelligence (MOI) Team helps AWS operate its services across the world. We help monitor AWS operations by providing insights and recommendations on AWS operations.
US, WA, Seattle
AWS Applied AI Solutions (AAIS) is where science meets customer obsession at scale. We build the intelligent systems that power AWS services used by millions, combining research in machine learning, agentic AI, and applied science with the operational rigor required to deliver enterprise grade experiences. Within AAIS, Amazon WorkSpaces is our cloud based virtual desktop service that delivers secure, managed computing to over one million daily users across the globe, enabling organizations to provision, manage, and scale desktops with the reliability and performance their workforce depends on. We are looking for an Applied Scientist to be part of the tiger team building the capacity modelling for Amazon WorkSpaces, owning and advancing the science behind it. You will design, build, and continuously improve the forecasting and optimization models that ensure the right compute, storage, and networking resources are available at the right time, in the right regions, at the lowest possible cost, without ever compromising the end user experience. This is a high impact individual contributor role for someone who thrives at the intersection of applied research and production systems. You will define the scientific roadmap for capacity intelligence, turning reactive provisioning into a predictive, self optimizing engine that anticipates demand before customers feel any constraint. Key job responsibilities Define and drive the scientific strategy for capacity modelling, establishing the research agenda that transforms how WorkSpaces forecasts demand, plans supply, and allocates resources across a globally distributed infrastructure. Build advanced demand forecasting models that predict workspace usage across multiple time horizons, from intraday spikes to long range growth trajectories, incorporating signals such as customer onboarding patterns, seasonal trends, regional expansion, and macroeconomic indicators. Design supply optimization frameworks that determine optimal resource placement, instance mix, and pre warming strategies, balancing availability, performance, and cost by reasoning over hardware constraints, pricing dynamics, and service level objectives. Develop causal and probabilistic models that move beyond trend extrapolation to true understanding of demand drivers, enabling the organization to distinguish organic growth from one time events, anticipate shifts in usage patterns, and quantify uncertainty in planning decisions. Architect simulation and scenario planning systems that allow business and engineering leaders to run what if analyses, stress test capacity plans against disruption scenarios, and evaluate trade offs between investment timing, risk tolerance, and customer experience. Pioneer the integration of machine learning with operations research, combining deep learning based forecasting with mathematical optimization to jointly solve the demand prediction and resource allocation problem in a way that neither discipline can achieve alone. Establish evaluation frameworks and monitoring systems that measure forecast accuracy, capacity utilization, and cost efficiency in production, creating tight feedback loops that drive continuous model improvement and build organizational trust in science driven planning. Influence the broader organization's capacity strategy by translating model outputs into actionable recommendations for leadership, identifying opportunities to extend capacity intelligence patterns to adjacent services, and mentoring scientists and engineers across the team. About the team As part of the AWS solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. we blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
Amazon Research Awards.jpg

Amazon Research Awards

Collaborating with scientists around the world to fund research, share knowledge and encourage innovation.