Careers

Application deadline: Aug 18, 2026 We are looking for an experienced Sr. Manager, Applied Science to own technical vision and roadmaps, drive innovation at AWS scale, and lead a team of applied scientists working at the frontier of automated reasoning and neurosymbolic AI. You will partner closely with security, privacy, and compliance stakeholders across AWS to expand the reach and impact of provably correct products. Key job responsibilities - Hire, develop, and retain a world-class team of applied scientists. - Foster a culture of scientific rigor, innovation, and operational excellence. - Own strategic design, implementation, and delivery of solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness of partner teams. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Science of Security is dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for security, privacy, and sovereignty.

We are looking for a talented Applied Scientist to join our team. In this role, you will design, develop, and deploy machine learning and computer vision models that solve real-world problems at scale in the Amazon grocery domain. You will work closely with engineering, product, and business teams to turn complex technical challenges into production-ready solutions, and own the model development lifecycle from experimentation through deployment. You will bring scientific rigor to every stage — from data analysis and model design to evaluation and iteration. This is a high-impact role where your models will directly improve the shopping experience for millions of customers in Amazon grocery stores. Key job responsibilities Design, train, and evaluate computer vision and machine learning models for complex grocery-domain problems including product identification, shelf perception, and in-store scene understanding — iterating rapidly from prototype to production-quality solutions Conduct rigorous exploratory data analysis to characterize domain-specific challenges (image variability, catalog gaps, label noise) and translate findings into actionable modeling decisions Own the model development lifecycle from experimentation through deployment — collaborating with software and ML engineers to ensure models meet latency, throughput, and reliability requirements at production scale Design and execute offline and online evaluation frameworks — defining metrics that capture both model performance and downstream business impact, and diagnosing failure modes to prioritize improvements Build and improve data pipelines and annotation workflows that feed model training, including active learning strategies to maximize label efficiency Communicate technical results, trade-offs, and recommendations clearly to engineering, product, and business stakeholders — connecting model behavior to customer experience outcomes Stay current with state-of-the-art research in computer vision, multimodal learning, and representation learning — evaluating and adapting promising techniques to team-specific problems Contribute to a culture of scientific rigor through reproducible experimentation, thorough documentation, peer code and design reviews, and raising the quality bar for the team A day in the life As an Applied Scientist on the GRAISE team, you'll spend your days analyzing model performance from overnight experiments, collaborating with engineers to deploy computer vision models to production, and prototyping new approaches using multimodal learning with store video and sensor data. You'll present findings to product and business stakeholders, translating technical results into actionable recommendations. Throughout the day, you'll balance rigorous scientific thinking with practical engineering constraints, knowing your work directly improves the shopping experience for millions of customers in Amazon grocery stores. About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency

We are looking for a Senior Applied Scientist who will own the science strategy and technical direction for computer vision and machine learning within the Amazon grocery ecosystem. You will identify the highest-impact problems in ambiguous, rapidly evolving domains, frame them rigorously, and drive solutions from research through production at scale. You will lead cross-functional technical decisions with engineering, product, and business partners, shaping not just what we build but also how we invest. This is a role where your scientific judgment, architectural choices, and ability to create clarity from ambiguity will directly define the intelligence layer powering millions of grocery shopping experiences. Key job responsibilities * Own the end-to-end science strategy for computer vision and machine learning solutions in the grocery domain, navigating ambiguity to identify the highest-impact opportunities * Develop novel approaches to complex, unsolved perception and identification challenges where off-the-shelf methods are insufficient; publish findings internally or externally to advance the state of the art * Define the evaluation framework and success criteria for model performance, establishing metrics that connect scientific outcomes to measurable business impact and using these to influence roadmap prioritization * Lead cross-functional technical design with engineering, product, and operations partners, driving architecture decisions for model serving, data pipelines, and system reliability at scale rather than solely handing off models for productionization * Identify and resolve ambiguous, cross-team technical dependencies (e.g., upstream data quality, annotation infrastructure, model interoperability) that block progress across multiple workstreams; propose and drive solutions proactively * Influence technical direction beyond the immediate team mentor scientists, raise the bar in hiring, establish best practices for experimentation and model development, and represent the team's science strategy to senior leadership * Communicate complex technical trade-offs and recommendations to VP-level stakeholders, shaping investment decisions and aligning cross-org partners on science-informed product direction A day in the life As a Senior Applied Scientist on the GRAISE team, you'll own the technical strategy for how computer vision and multimodal learning come together to solve perception problems in grocery stores — many of which no one has cleanly formulated yet. On any given day, you might diagnose a surprising failure mode from overnight experiments and decide whether to pivot your approach entirely, co-architect a serving system with engineers while defining confidence thresholds and graceful degradation paths, present a precision-recall trade-off to senior leaders in terms that shape launch decisions and investment priorities, or unblock a cross-team dependency on annotation infrastructure — all while mentoring junior scientists and carving out time for deep technical work on problems the team hasn't cracked. Your scientific judgment and architectural choices will directly shape the shopping experience for millions of customers across Amazon's grocery stores. About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency

We are looking for a talented Applied Scientist to join our team. In this role, you will design, develop, and deploy machine learning and computer vision models that solve real-world problems at scale in the Amazon grocery domain. You will work closely with engineering, product, and business teams to turn complex technical challenges into production-ready solutions, and own the model development lifecycle from experimentation through deployment. You will bring scientific rigor to every stage — from data analysis and model design to evaluation and iteration. This is a high-impact role where your models will directly improve the shopping experience for millions of customers in Amazon grocery stores. Key job responsibilities Design, train, and evaluate computer vision and machine learning models for complex grocery-domain problems including product identification, shelf perception, and in-store scene understanding — iterating rapidly from prototype to production-quality solutions Conduct rigorous exploratory data analysis to characterize domain-specific challenges (image variability, catalog gaps, label noise) and translate findings into actionable modeling decisions Own the model development lifecycle from experimentation through deployment — collaborating with software and ML engineers to ensure models meet latency, throughput, and reliability requirements at production scale Design and execute offline and online evaluation frameworks — defining metrics that capture both model performance and downstream business impact, and diagnosing failure modes to prioritize improvements Build and improve data pipelines and annotation workflows that feed model training, including active learning strategies to maximize label efficiency Communicate technical results, trade-offs, and recommendations clearly to engineering, product, and business stakeholders — connecting model behavior to customer experience outcomes Stay current with state-of-the-art research in computer vision, multimodal learning, and representation learning — evaluating and adapting promising techniques to team-specific problems Contribute to a culture of scientific rigor through reproducible experimentation, thorough documentation, peer code and design reviews, and raising the quality bar for the team A day in the life As an Applied Scientist on the GRAISE team, you'll spend your days analyzing model performance from overnight experiments, collaborating with engineers to deploy computer vision models to production, and prototyping new approaches using multimodal learning with store video and sensor data. You'll present findings to product and business stakeholders, translating technical results into actionable recommendations. Throughout the day, you'll balance rigorous scientific thinking with practical engineering constraints, knowing your work directly improves the shopping experience for millions of customers in Amazon grocery stores. About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency

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? We are looking for a Research Scientist who will be responsible to develop cutting-edge scientific solutions to optimize our fulfillment strategy across multiple regions of the world (EU, JP, IN and more), to maximize our Customer Experience and minimize our cost and carbon footprint. You will partner with the worldwide scientific community to help design the optimal fulfillment strategy for Amazon. You will also collaborate with technical teams to develop optimization tools for network flow planning and execution systems. Finally, you will also work with business and operational stakeholders to influence their strategy and gather inputs to solve problems. To be successful in the role, you will need deep analytical skills and a strong scientific background. The role also requires excellent communication skills, and an ability to influence across business functions at different levels. You will work in a fast-paced environment that requires you to be detail-oriented and comfortable in working with technical, business and technical teams.

Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions. There are numerous scientific and technical challenges you will get to tackle in this role, such as optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning. As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of. You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques. - Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. - Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. - Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. - Develop offline policy estimation tools and integrate with measurement systems/econometric models. - Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. - Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. - Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. - Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems. Key job responsibilities - Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. - Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. - Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. - Develop offline policy estimation tools and integrate with measurement systems/econometric models. - Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. - Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. - Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. - Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.

Stores Economics and Science (SEAS) is an interdisciplinary team in Amazon's Stores organization with a peak-jumping mission: we apply expertise in science and engineering to move from local to global optima in methods, models, and software. We pursue this mission by leveraging frontier science, collaborating with partner teams, and learning from the tools, experience, and perspective of others. We scale by solving problems, first in the small to prove concepts, and then in the large by building scalable solutions. We also help other teams within Amazon scale by hiring and developing the best and embedding them in other business units. We are looking for a Senior Economist to drive high-impact economic analysis and modeling that shapes how Amazon's Stores business makes decisions. In this role, you will work in a team of economists, scientists, and engineers to identify key business questions, design rigorous analytical frameworks, and deliver actionable insights to senior leadership and partner teams. You will own end-to-end research (from problem formulation and data analysis through modeling and stakeholder communication) in areas such as pricing, demand estimation, substitution measurement, and experiment design. Your responsibilities include developing economic models and empirical analyses that inform strategic decisions, designing and analyzing experiments, and translating complex findings into clear recommendations for technical and non-technical audiences. You will also mentor junior economists and help raise the bar on economic rigor across partner teams. The ideal candidate has a PhD in Economics and deep expertise in causal inference and applied econometrics. Experience with large-scale data, proficiency in statistical programming (Python or similar), and familiarity with machine learning methods are a plus. To be successful in this role, you should be comfortable operating with ambiguity, able to independently scope and prioritize research agendas, skilled at influencing decisions through rigorous analysis, and comfortable with using AI tools.

Amazon.com is seeking an exceptional Senior Economist to join our Advertising Finance team. As a tech lead of the Adpt Finance Econ and Science team, you will play a pivotal role in answering critical questions that drive the long-term strategy of our advertising business. These questions include: - What are the long-term impacts of our initiatives? - Where will Advertising’s growth come from in the next year? - How big will the Advertising business become over the next three years? - What are the interactions between consumers and the Ads business? At Amazon, we're always finding answers that redefine industries. In this Senior Economist role, you'll have the unique opportunity to collaborate with top-tier talent, influence senior leadership, and make a tangible impact on the future of advertising. If you're passionate about pushing the boundaries of economic research, thrive on challenging modeling puzzles, and crave a dynamic environment where your insights directly shape business strategy, this is the role for you. Key job responsibilities - Lead causal analysis projects as the primary technical expert, guiding the team in applying advanced economic methodologies to solve complex business challenges. - Collaborate closely with economists, data scientists, financial managers, and business leaders to define product requirements, offer scientific support, and effectively communicate feedback throughout project lifecycles. - Utilize programming languages such as Python, R, Scala, etc., to implement sophisticated economics methods tailored to address specific business problems, ensuring robustness and scalability. - Drive continuous improvement by innovating existing methodologies, including developing new data sources, rigorously testing model enhancements, and fine-tuning model parameters to optimize performance and accuracy. - Present data and insights in a clear, actionable format, enabling stakeholders to make informed decisions and address critical business questions with confidence.

Amazon Prime is building the future of AI-based personalization in driving long-term value out of the Prime subscription. Join our team of Scientists and Engineers developing AI/ML models to predict the Prime customer long-term behavior and optimize the customer experience with the Prime program. This includes identifying who our customers are, modeling customer behavior, and creating personalization systems to optimize the experience. As an AI/ML expert, you will partner directly with product owners to define the vision for this space and drive the roadmap for team execution. This is a complex personalization and optimization challenge. We sit at the intersection of massive-scale subscription and behavioral data, real-time decision systems, hundreds of millions of customers, and intersection with other shopping and benefit data across Amazon. Our vision is to enable a data-driven intelligent foundation that powers the future of Prime through scalable personalization, generation, and decisioning capabilities that serve optimal, cohesive customer experiences across all touchpoints. We build the science and infrastructure that allows Prime to know each member deeply, decide what to show them and when, generate the right content in the right format, and measure whether it worked — all optimized toward long-term member value. As a Principal Applied Scientist, you will be the intellectual engine behind this transformation. You will define the frontier, architect the science strategy for a large, cross-cutting organization, and drive breakthroughs that reshape how Prime thinks about customers and the membership program at the deepest level. You will pioneer next-generation Gen AI / LLM-based foundation models that build rich, evolving models of customer intent. This will include building Gen AI/transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. It will require thinking about real-time, multi-objective, globally scalable ranking systems that address cold start problems at billions of decisions per day. You will do all of this as a force multiplier, building foundational technology that empowers scientists and engineers, and driving teams across Prime and Amazon to deliver the best science outcomes for customers. Come define what the Prime membership program and experience looks like in the age of AI!

Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? If so, we have an exciting opportunity for you! Translation Services is seeking an Applied Science Manager to own the technical vision and multi-year science roadmap spanning machine translation, multimodal content (image translation, video subtitling), and automated quality evaluation. This leader will manage scientists and MLEs, define research direction for novel problem spaces with limited industry precedent, and bridge science breakthroughs into production-ready systems operating at Amazon scale. As a leader of the Science team of TS, this person will be responsible for leading their team in designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The goal is to build solutions with minimal human touch involved in any language translation and ensure accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. This role requires strong technical skills, a deep understanding of machine learning approaches, and a solid grasp on NLP and LLM techniques to solve complex language translation challenges. You must have a demonstrated ability for optimizing, developing, launching, and maintaining large-scale production systems. As a key member of the team, you will oversee all aspects of the software lifecycle: design, experimentation, implementation, and testing. You should be willing to dive deep when needed, move rapidly with a bias for action, and get things done. You should have an entrepreneurial spirit, know how to deliver, and long for the opportunity to build pioneering solutions to challenging problems. This role will demand resourcefulness and willingness to learn on both the technical and business side. Key job responsibilities In this role, you will work closely with business partners, applied scientists, software development engineers, and product managers to accelerate building solutions to expand translation capabilities. You will have significant influence on our overall strategy by helping define science and engineering strategy, define product features, drive system architecture, and spearhead the best-practices that enable a quality product. You will also influence the development processes, and develop well-rounded skills such as leadership, and effective project management. Building a strong development team and developing career plans for the scientists and engineers reporting to you will be a key responsibility. Throughout, you should possess creativity, curiosity, and excellent judgment to thrive in an environment of ambiguity. A day in the life You will spend your days collaborating with scientists, developers, customers, stakeholders, and converting the business needs into a data-driven solution. You will support a team to design and execute science products. You will dive deep into the data and balance technical execution with longer term strategy. You will grow and develop your team. About the team Translation Services is entering a phase where the problems ahead are fundamentally different from the problems we've solved. Our text translation stack is production-grade and serving 30+ language pairs across Retail. But the next frontier — image translation, video subtitle localization, long form text and automated quality evaluation — represents novel research problems at Amazon scale with limited industry precedent.