Careers

At Amazon, Security is a top priority. Are you interested in shaping the future of security for AWS, our customers, and the broader open source community? Do you want to be part of building something with massive impact? Our AWS Security Customer Outcomes Team is looking for a strong applied scientist as we help improve the security of open source software and the safety of the software supply chain generally. We're looking for an ambitious team member who will bring added scientific rigor and innovation to how our customer's experience security with and through open source software. Our systems operate on a global scale across a diverse ecosystem of field teams, security requirements, and tooling which requires high velocity security innovation that scales. We are seeking an Applied Scientist to help define, design, build, and operate AI-powered security solutions across the full lifecycle of open source software usage. You will work alongside a team to build intelligent automation that augments manual identification, analysis, and remediation workflows, enforces security controls for agents and human experts, and provides data-driven insights to AWS leadership. Key job responsibilities - Architect and build AI-powered security applications and tooling — Design and implement LLM-based systems (leveraging Bedrock, SageMaker, and state of the art patterns) for intelligent code scanning, automated solution development/deployment, and remediation alternatives. - Transform manual processes – Reimagine and deliver change from low throughput, human-dependent processes into high volume, machine speed pipelines without sacrificing key quality metrics. - Collaborate with and influence other leaders – The open source ecosystem has many stakeholders, and change requires listening and supporting but also influencing and leading through action. - Drive proactive security automation — Build systems that identify and remediate security issues in deliverables (code, threat models, content) without requiring explicit builder consent at every step, moving from reactive reviews to proactive prevention - Own operational excellence — Define and maintain SLAs, monitoring, alerting, runbooks, and incident response for services you own; participate in on-call rotation to support 24/7 availability - Lead technical design — Produce design documents, conduct trade-off analysis, and drive alignment across organizations. - Design and conduct scientific research using machine learning and deep learning techniques to address complex, ambiguous problems, working backwards from customer needs to invent new approaches or extend existing ones. - Build, train, and evaluate production-quality models using frameworks such as PyTorch or TensorFlow, applying rigorous experimentation, feature engineering, and hyperparameter optimization to improve accuracy and performance. - Write clean, maintainable code with optimal data structures and algorithms, ensuring your components integrate directly into production systems and meet high standards for operational reliability and resource efficiency. - Collaborate with engineering and product partners to translate scientific insights into scalable solutions, clearly communicating design decisions and trade-offs to ensure long-term maintainability. - Contribute to the scientific community by authoring or co-authoring peer-reviewed publications, mentoring less experienced scientists, and participating in technical reviews across teams. A day in the life You might start your morning reviewing experiment results and refining a model architecture before syncing with engineering partners on an upcoming production integration. After lunch, you could dive into a research paper relevant to a challenge your team is tackling, then prototype a new approach and set up evaluation pipelines. Later, you may join a design review to share your findings or pair with a teammate to debug a tricky data pipeline issue. Throughout, you balance hands-on scientific exploration with collaborative problem-solving. About the team At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the 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 Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

The NASC & TOM Science team owns Operations Research, Machine Learning, and AI projects across the North America Sort Center (NASC) and Transportation Operations Management (TOM) planning and operations organizations. We turn complex network, labor, and capacity problems into deployed models that drive multi-million-dollar planning decisions every day. As a Data Scientist II, you will own the end-to-end Machine learning Operation cycle: Design, build, and ship machine learning and/or optimization models that directly shape Amazon's middle miles planning decisions. You will own end-to-end delivery — from problem framing with business partners, through modeling and validation, to deployment in internal model hosting platform and integration with downstream planning tools. You will work on problems such as: Long- and short-horizon forecasting Network and capacity optimization GenAI / agentic systems Defect prevention and adaptive planning You will partner closely with Engineering, Product, Engineering, and stakeholders to translate ambiguous operational pain points into measurable model outcomes. Key job responsibilities - Design and implement complex ML and optimization solutions (forecasting, MIP/LP, simulation, Deep learning / foundation model) - Drive end-to-end delivery of scalable models. From data exploration and feature engineering through training, evaluation, deployment, and post-launch monitoring; - Develop new modeling patterns and analytical frameworks for forecasting (multivariate, hierarchical, causal-DAG, model-chaining) and optimization; - Build robust model validation, backtesting, and monitoring pipelines; identify and eliminate sources of leakage, bias, and silent failure; - Define and own model performance metrics (e.g., WAPE) tied to business outcomes; - Partner with Data Engineering and Software Development to productionize models and define I/O contracts, packaging, and model CI/CD; - Excellent communication to present findings, tradeoffs, and recommendations clearly to stakeholders and senior leadership.

The NA AMZL Supply Chain organization leads the innovation of Amazon’s Last Mile. We are an Operations org that hires and manages associates to deliver packages next day and sub-same day. Our key vision is to transform the online experience by enabling ultra-fast delivery at lower cost possible. We’re growing in scale and volume, by orders of magnitude. The Execution and Planning Science-Engineering (EPSE) team sits within NA AMZL Supply Chain with the mission to build world-class automated Science-Tech products that enable ultra-fast delivery speeds for Amazon customers and job market opportunities for Amazon associates. We are team of Scientists, Engineers and Tech leaders who invent and implement scalable algorithms for automated decision making in the Operations space. We are looking for an Applied Scientist to contribute to the future of Last Mile associate labor planning. Work will include advising on evolving flexible labor planning programs, optimizing our supply chain network, providing data-driven strategies to improve cost, reliability or speed. Designing, implementing and evaluating experiments, and measuring impact. Work will feed strategic analyses and also be part of production systems. Experience needed in Optimization and Statistics/ML. Key job responsibilities As an Applied Scientist, you’ll design, model, develop and implement state-of-the-art models and solutions. As part of your role you will regularly interact with software engineering teams and business leadership.

Application deadline: Oct 3, 2026 Are you excited about using data to shape decisions that reach millions of customers? Do you thrive on solving ambiguous, high-impact problems with creative analytical approaches? As a Data Scientist III at Amazon, you will lead the design and delivery of data science solutions that drive measurable business outcomes. You will apply your broad expertise across machine learning, statistical modeling, and experimentation to tackle complex challenges and uncover opportunities. In this role, you will influence business strategy through rigorous analysis, recommend the right scientific methods for each problem, and set standards that raise the bar for your team. You will collaborate across teams to understand how systems and processes interact, and you will build models and analyses that are actionable, extensible, and easy for others to build upon. Key job responsibilities - Lead the design and implementation of data science solutions for complex or ambiguous problems, applying machine learning, statistical modeling, and experimentation to deliver measurable business impact. - Evaluate cross-team perspectives and model interactions among teams, processes, and systems to recommend the right data science strategy and methods for each challenge. - Drive data science best practices and set standards across your team, including building reproducible models and analyses that others can contribute to and extend. - Mentor and develop other data scientists by reviewing their analyses, providing technical feedback, and sharing expertise through presentations and knowledge-sharing sessions. - Identify gaps in current metrics and propose new measurements or data sources, challenging assumptions and proactively restructuring approaches to resolve root causes of recurring problems. A day in the life You might start your morning reviewing the results of a model you recently deployed, assessing its performance against key business metrics. Later, you join a cross-functional meeting to align on the analytical approach for a new initiative, translating business questions into well-defined scientific problems. In the afternoon, you pair with a teammate to debug a data pipeline issue, then spend time prototyping a new algorithm. You close the day by writing up findings for a stakeholder review, ensuring your recommendations are clear and backed by data. About the team Our team designs and engineers high-profile consumer electronics, including the best-selling Kindle family of products. We have also produced groundbreaking devices like Fire tablets, Fire TV, Amazon Dash, and Amazon Echo. Our team is focused on using data science to help Amazon make better, faster decisions for our customers. We value collaboration, intellectual rigor, and a willingness to question the status quo. You will work alongside scientists, engineers, and business partners who are passionate about turning data into action. We are investing in new modeling capabilities and measurement frameworks, and your contributions will directly shape the direction of that work. If you are looking for a role where your expertise will be valued and your growth supported, we would love to hear from you.

Are you a PhD student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical 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. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. For more information on the Amazon Science community please visit https://www.amazon.science.

Are you a Master's student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical 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. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. For more information on the Amazon Science community please visit https://www.amazon.science.

Unleash Your Potential at the Forefront of AI Innovation At Amazon, we're on a mission to revolutionize the way the world leverages machine learning. Amazon is seeking graduate student scientists who can turn revolutionary theory into awe-inspiring reality. As an Applied Science Intern focused on Information and Knowledge Management in Machine Learning, you will play a critical role in developing the systems and frameworks that power Amazon's machine learning capabilities. You'll be at the epicenter of this transformation, shaping the systems and frameworks that power our cutting-edge AI capabilities. Imagine a role where you develop intuitive tools and workflows that empower machine learning teams to discover, reuse, and build upon existing models and datasets, accelerating innovation across the company. You'll leverage natural language processing and information retrieval techniques to unlock insights from vast repositories of unstructured data, fueling the next generation of AI applications. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara, CA; Seattle, WA. Key job responsibilities We are particularly interested in candidates with expertise in: Knowledge Graphs and Extraction, Neural Networks/GNNs, Data Structures and Algorithms, Time Series, Machine Learning, Natural Language Processing, Deep Learning, Large Language Models, Graph Modeling, Knowledge Graphs and Extraction, Programming/Scripting Languages In this role, you'll collaborate with brilliant minds to develop innovative frameworks and tools that streamline the lifecycle of machine learning assets, from data to deployed models in areas at the intersection of Knowledge Management within Machine Learning. You will conduct groundbreaking research into emerging best practices and innovations in the field of ML operations, knowledge engineering, and information management, proposing novel approaches that could further enhance Amazon's machine learning capabilities. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to 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. A day in the life - Develop scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Design, development and evaluation of highly innovative ML models for solving complex business problems. - Research and apply the latest ML techniques and best practices from both academia and industry. - Think about customers and how to improve the customer delivery experience. - Use and analytical techniques to create scalable solutions for business problems.

IES Payments is building a first-of-its-kind Economic Profit (EP) framework to measure the full economic value of Amazon Payment Products (APPs) and payment-led promotional levers across 9 emerging markets. Today, program leaders cannot credibly quantify the total value their products create beyond direct revenue contribution — EP changes that. We are looking for an Economist who will own the incrementality science that underpins this framework. You will design, evolve, and defend the econometric methodologies that measure how payment instruments create value — within the transaction (Day-0) and over the 365-day customer lifetime (DSI). You will be the single-threaded science owner for how we compute and justify incrementality across always-on instruments, promotional constructs, weblabs, and behavior-change programs. This is not a support role. You will directly shape how Amazon measures the ROI of its payment investments across IES — and your work will feed into S-Team level planning and investment decisions. Key job responsibilities What You'll Do Incrementality Methodology — Design & Evolve ● Own end-to-end design of incrementality computation logic across EP use cases: always-on APPs, one-time promotions, weblab measurement, and ongoing behavior-change programs (e.g., UPI adoption rewards) ● Evolve the Day-0 incrementality methodology — the novel component that captures value created within the current transaction itself, where standard DSI excludes the event day ● Design econometric approaches for use cases where GCCP fails (weblabs with instrument dynamics) and where traditional promo measurement breaks down (investment-phase → payoff-phase programs) ● Develop and validate proxy logic for segment-wise EP cuts in the absence of dedicated compute infrastructure CBA & Cross-Functional Alignment ● Lead the formal methodology defense with CBA V-Team — every surrogate, every computation, every modification to Watson's default workflow requires rigorous scientific justification and CBA sign-off ● Align with DEX and Stores Finance on the science behind cost-to-serve incrementality metrics (CoP avoidance, UPB savings, CoBW reduction) ● Navigate the surrogate onboarding challenge: new payment surrogates can cannibalize value currently attributed to existing Marketplace HVAs — you will need data-backed arguments to bring Stores leadership on board, not just science correctness ● Build and maintain the defensibility of EP methodology against internal scientific scrutiny Partnership with Data Science ● Work hand-in-hand with the EP Data Scientist to build a holistic case for payment HVA incrementality — you bring the econometric logic, they bring the predictive model justification; both must align for CBA acceptance ● Jointly defend surrogate onboarding proposals where the economist provides the causal framework and the data scientist provides the feature-level model evidence ● Translate econometric findings into business narratives for VP+ leadership reviews Geographic Expansion ● Adapt and calibrate EP methodology for Brazil, Mexico, MENA, and APAC markets — each with distinct instrument mixes, data maturity levels, and marketplace economics ● Determine where the India-built methodology transfers cleanly and where market-specific econometric adaptations are needed About the team Our team is dedicated to applying economic thinking to some of Amazon's most impactful business questions. We bring together economists with expertise spanning causal inference, structural modeling, and applied microeconomics to deliver insights that guide long-term strategy. We value intellectual rigor, open debate, and a collaborative spirit that makes everyone stronger. We are building toward a future where economic analysis is deeply embedded in decision-making at every level. If you want to work alongside thoughtful, curious colleagues and see your work shape how Amazon operates, this is the team for you.

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? Have you also wondered what are different ways that the transportation assets can be used to delight the customer even more. If so, the Amazon transportation Services, North America Sort Center Science 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 Applied Scientist with strong scientific thinking, good software and statistics experience, skills to help manage projects and operations, improve metrics, and develop scalable processes and tools. The primary role of an Applied 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 we operate the middle mile network. 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, machine learning , and the ability to use data and research to make changes. This role requires robust skills in research and implementation of scalable products and models . 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. Key job responsibilities Key job responsibilities - Design and develop advanced mathematical optimization and machine learning solutions in the domains of network design and optimization. - Research, prototype, simulate, and experiment with these models using programming languages such as Java and Python; participate in the production level deployment. - Create, enhance, and maintain technical documentation and science designs. - Present to other Scientists, Product, and Software Engineering teams, as well as Stakeholders. - Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans.

Amazon's Artificial General Intelligence (AGI) organization builds frontier models and the AI agents on top of them, and every one of them depends on data we can trust. The Frontier AI (FAI) Assessment team owns the science of dataset quality — for the data that trains frontier models, and for the benchmarks that determine whether a model or an agent actually works. In this role you will build the automated systems that make quality assessment at scale. Large language model (LLM)-as-a-Judge is the starting point, and our goal is to develop an agentic system that plan its own audits, critiques its own judgments, and improves its own accuracy over time. You will design it, calibrate it against expert human judgment, and set the technical direction for how the organization measures data quality. Key job responsibilities - Design the assessment methodology for frontier AI assets - Build the automated assessment system, from LLM-as-a-Judge baselines to agents that plan their own audits and learn from their own errors - Oversee the expert audit program and raise the proficiency of the auditors who run it - Design the measurement methods that make quality findings defensible, including sampling strategy and error analysis - Communicate findings to the teams that create the data and to the teams that train models and build agents using it - Set technical direction for assessment science, mentor junior scientists, and present results to senior leadership - Publish research on assessment methodology at top-tier venues and contribute to patents A day in the life - Train AI agents to assess the quality of datasets and evaluation benchmarks, and document where they fall short - Diagnose their failures and improve the models, rubrics, and calibration behind them - Review the quality reports that auditors and agents produce, and decide whether the conclusions hold - Coach expert auditors and junior scientists, and move more of the manual audit work into automation - Lead research on self-improving assessment agents, from open problem to publication About the team FAI Assessment is part of Frontier AI Assets in AGI. We assess the quality of the datasets and benchmarks behind Amazon's frontier models and agents, and we define what good data means. Today that work relies on human-in-the-loop review by domain experts. Our aim is to automate it with self-improving agents that experts keep calibrated. We are a small team of scientists working closely with the data, modeling, and agent teams.