Careers

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: Nov 1, 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.

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.

Amazon Devices Sensor Algorithms team is seeking a Senior Applied Scientist to pioneer sensor-based algorithms that power next-generation experiences across Amazon's device ecosystem, including Echo, Kindle, Fire TV, and Fire Tablets. Working with multidisciplinary teams of scientists and engineers, you'll develop innovative technologies at the intersection of signal processing and machine learning that transform how millions of customers interact with our products. The ideal candidate combines strong theoretical foundations in machine learning and signal processing with practical implementation skills. You'll develop state-of-the-art sensor algorithms from concept to production, translate complex research problems into practical consumer technologies, and create solutions optimized for diverse hardware platforms. We are looking for someone who thrives in fast-paced environments, solves complex problems efficiently, and iterates quickly based on real-world feedback. Your technical decisions will directly shape future product capabilities and deliver exceptional experiences to Amazon customers worldwide. Key job responsibilities - Develop and implement advanced algorithms and machine learning models to enhance Amazon's products and services. - Collaborate with cross-functional teams, including software engineers, scientists, and product managers to translate business needs into technical solutions. - Conduct thorough data analysis to identify trends, patterns, and insights that drive product innovation and improvement. - Optimize algorithms for performance, scalability, and efficiency across various Amazon platforms. - Present findings and recommendations to stakeholders, influencing product strategy and decision-making. - Stay abreast of the latest research and technological advancements in machine learning and related fields to continuously improve Amazon's offerings. - Mentor and guide junior scientists and engineers, fostering a culture of learning and innovation. - Ensure the ethical use of data and algorithms, adhering to Amazon's guidelines and best practices. - Contribute to the publication of research findings in conferences and journals, elevating Amazon's reputation in the scientific community. About the team At Amazon Lab126, we're a pioneering research and development hub dedicated to designing and engineering revolutionary consumer electronics. Established in 2004 as a subsidiary of Amazon.com, Inc., we've been at the forefront of innovation, starting with the creation of the best-selling Kindle family of products. Our portfolio has since expanded to include transformative devices such as Fire tablets, Fire TV, and Amazon Echo. Our Lab126 team is dedicated to developing advanced sensing technologies and algorithms, collaborating with program managers to design and implement transformative user features and experiences.

Core to Amazon’s DNA is its methodology around executing experiments and prototypes to find the quickest way to prove new ideas, technologies, and business models in a real-world environment. AWS Specialist Prototyping team provides hands-on support to qualified customers & partners to discover the art of the possible with AWS and accelerate their path to production. If you are a highly creative problem solver who works to inspire, motivate, and define innovative solutions; are passionate about technology, understand cloud architectures & platforms, and are quick to pick up emerging technologies; are adept at working with customers to experiment with innovative approaches, and validate the technical feasibility of solutions - this could be the role for you! Key job responsibilities A Sr. Applied Science Manager in the AWS Specialist Prototyping team sets the science direction for the team on emerging tech that represents the most innovative and challenging use cases for our customers. They ensure the success of this unique customer facing applied science execution team by serving as the engagement manager responsible for sourcing, qualifying, scoping, executing, delivering and transitioning Prototyping engagements. They drive science prototypes that have a high degree of ambiguity, scale and complexity. They provide technical and science leadership related to large language models, generative multi-modal models and computer vision. They recruit high performing Applied Scientists to the team and provide mentorship. They establish team mechanisms, including team building, planning, and document reviews. About the team The Worldwide Specialist Organization (WWSO) is part of AWS and is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. We work backwards from our customer’s most complex and business critical problems to build and execute go-to-market plans that turn AWS ideas into multi-billion-dollar businesses. WWSO teams include business development, specialist and technical solutions architecture. As part of WWSO, you'll provide expertise across the entire life cycle of an AWS customer initiative, from developing ideas for new services to accelerating the adoption of established businesses. We pride ourselves on thinking big, delivering exceptional results for our customers, and working across AWS as #OneTeam. Within WWSO, the AWS Specialist Prototyping team's mission is to accelerate customer innovation by transforming complex business challenges into novel AWS solutions through rapid prototyping and experimentation. Get a sample of the work we have done with customers here: https://www.youtube.com/watch?v=OqIGzNtM670&list=PL9NTgqXfVhb79tN35dpbKSO-q4OdfBu_d Large language models we have created are here: https://huggingface.co/aws-prototyping/ 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 Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) conferences, inspire us to never stop embracing our uniqueness. 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 in the cloud.

Are you interested in shaping the future of entertainment through cutting-edge AI? Prime Video’s technology teams are redefining the digital video experience at scale. As a Senior Manager, Applied Scientist at Prime Video, you will be a technical and strategic leader responsible for inventing, developing, and deploying groundbreaking AI solutions that power personalized, relevant, and delightful experiences for millions of global customers. You will help shape the vision and direction of key ML systems that support Prime Video’s mission to deliver AI-powered customer experiences. This role demands a unique blend of deep technical expertise in machine learning and recommendation systems, industry leadership, and strong collaboration skills. You will guide the development of high-impact systems end-to-end - leading innovation from foundational research through production deployment - while mentoring scientists and influencing product and engineering roadmaps. We are looking for a thought leader who brings a strong track record of delivering ML innovations at scale, along with the curiosity and drive to push boundaries. This is a rare opportunity to drive meaningful impact at one of the largest streaming services in the world. Key job responsibilities As a Sr. Manager, Applied Science, in the Prime Video Personalization and Discovery organization, you will be responsible for optimizing the complete customer experience, across the touch points throughout customers’ discovery journey. This includes building AI and optimization solutions, working with product, engineering teams to deliver the optimal balance of customer delight and business outcomes. About the team Prime Video Personalization and Discovery (PVPD) is dedicated to creating a highly personalized content discovery experience that not only delights our customers but also drives both short-and long-term business goals. Our scope includes personalized recommendations, search, marketing, and the advanced machine learning technology and infrastructure that underpins these experiences. Our mission is to automate and enhance customer engagement through personalization, using ML and Generative AI.