Careers

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!

Amazon Ads is re-imagining advertising through generative artificial intelligence (AI) technologies. We combine human creativity with AI to transform every aspect of the advertising life cycle, from ad creation and optimization to performance analysis and customer insights. Our solutions help advertisers grow their brands while enabling millions of customers to discover and purchase products through delightful experiences. We deliver billions of ad impressions and millions of clicks daily, breaking fresh ground in product and technical innovations. 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. Why you’ll love this role: Amazon is investing heavily in building a world-class advertising business. To meet Advertiser goals, we process over 4 million ad request per second. This team defines and delivers optimization solutions for this enormous volume of traffic filtering unnecessary ad request and processing only whats in-demand and brings value for our Advertisers. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. You'll work alongside talented engineers, and product leaders in a culture that encourages bias for action, innovation and experimentation, and you will directly influence business strategy through your scientific expertise. What makes this role unique is the combination of scientific rigor with real-world impact. You will re-imagine advertising through the lens of advanced ML while solving problems that balance the needs of advertisers, customers, and Amazon's business objectives. Your impact and career growth: Amazon Ads is investing heavily in AI and ML capabilities, creating opportunities for scientists to innovate and make their marks. You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Learn more about Amazon Ads: https://advertising.amazon.com/ Key job responsibilities As a Senior Applied Scientist in Amazon Ads, you will: - Conduct hands-on data analysis, build large-scale machine-learning models and pipelines - Build ML models, perform proof-of-concept, experiment, optimize, and deploy your models into production, working closely with cross-functional teams including engineers, product managers, and other scientists - Design and run A/B experiments to validate hypotheses, gather insights from large-scale data analysis, and measure business impact. - Research and implement ML approaches, including applications of generative AI and large language models. - Drive end-to-end optimization projects that tackle ambiguous problems at massive scale, often working with petabytes of data. - Provide technical leadership, research new machine learning approaches to drive continued scientific innovation - Build supply optimization models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Develop scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience

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.

Reinventing How the World Shops! We are building the future of human-AI collaboration in commerce. We are creating an AI-native shopping partner that truly understands what customers mean, what they need, and what they haven't yet realized they want—rivaling the intuition of the best human experts, operating at a scale no human ever could. This is a complex personalization challenge. We sit at the intersection of massive-scale language understanding, real-time decision systems, hundreds of millions of customers, and billions of products. Our mission is to collapse the distance between intent and discovery—to make the leap from "searching for products" to "being understood as a person." 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, multidisciplinary organization, and drive breakthroughs that reshape how Amazon thinks about customers and products at the deepest level. The problems you'll solve don't have textbook answers. You will pioneer next-generation LLM-based reasoning systems that build rich, evolving models of customer intent. You will design transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. You will invent real-time, multi-objective ranking systems that balance exploration, personalization, and serendipity at billions of decisions per day. And you will do all of this as a force multiplier, building foundational technology that empowers teams across Amazon to deliver experiences that feel almost magical. Your work will be felt, not just measured. Every model you build ships directly to hundreds of millions of customers. The feedback loop between your science and real human delight is immediate. This role offers a rare combination of intellectual depth, technical ambition, and tangible impact. Come define what shopping looks like in the age of AI! Key job responsibilities - Innovate new features and models that have huge impact on the customer experience. Help customers find the right products and content on their shopping journey. - Leverage the use of advanced machine learning to create customer shopping experience at Amazon's scale - for all Amazon customers across all countries in realtime - Be a key leader on a multidisciplinary team across science, product, design, and engineering to see through ideas from inception, prototype, to launch in the hands of all Amazon's customers - Drive the science roadmap across multiple teams, helping coordinate a cohesive science agenda across the org. - Mentoring applied scientists across the org, growing their skills and careers. About the team Our mission is to delight every Amazon customer with a personalized shopping experience tailed to their intent. We achieve our mission through investments in Science, UX, and central systems with the purpose of delivering the future of shopping on Amazon. We are seeking a Principal Applied Scientist to lead the science charter across the recommendations and intent identification space.

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.

We are looking for an Applied Scientist to join the Robotics Simulation team at Amazon Robotics. In this role you will design, build, and validate the simulation environments and policy training pipelines that enable robots to learn manipulation and mobility skills in simulation and transfer them to real hardware. You will work at the intersection of robotics simulation science and modern Physical AI: building GPU-accelerated RL environments, implementing imitation learning workflows, characterizing sim-to-real gaps, tuning physics parameters against real-world data, and evaluating learned policies both in simulation and on physical robots. You will collaborate closely with SDEs who build platform infrastructure, Technical Artists who create simulation assets, and partner science teams who consume your environments and pipelines for their model development. This is a hands-on, execution-focused role. You will own specific simulation science deliverables end-to-end, from environment design through policy evaluation, with increasing scope and independence over time. You will contribute to technical design discussions, propose improvements to the team's simulation fidelity and training methodology, and help establish best practices for robot learning in simulation. Key job responsibilities * Design and implement GPU-accelerated reinforcement learning and imitation learning environments in NVIDIA Isaac Lab for manipulation and mobility tasks. * Build and maintain policy training pipelines supporting diverse model architectures (diffusion policies, VLAs, behavior cloning, actor-critic RL) and evaluate trained policies in simulation. * Characterize and reduce sim-to-real gaps through systematic validation: compare simulated sensor outputs, kinematics, and dynamics against real-world robot data, then implement targeted improvements. * Implement domain randomization strategies (visual, physics, geometric) to improve policy robustness and transfer to real hardware. * Develop sim-to-real transfer techniques including system identification, physics parameter calibration, and visual domain adaptation. * Create robot embodiment validation tests (joint kinematics, actuator response, contact behavior) to ensure digital twins are faithful to real hardware. * Build data pipelines for recording, replaying, and augmenting demonstration data (from teleoperation or automated trajectory generation) to scale training data volume. * Contribute to end-effector modeling and contact dynamics tuning, ensuring physically plausible gripper and tool interactions in simulation. * Author design documents for new simulation science capabilities and contribute to technical reviews. * Collaborate with partner science teams to understand their model architectures and ensure simulation environments meet their training requirements. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team The Robotics Simulation team is a multidisciplinary organization of SDEs, Applied Scientists, and Technical Artists at Amazon Robotics. We build the simulation infrastructure that powers Physical AI development, from photorealistic synthetic data to GPU-accelerated training environments. Our simulation stack enables robots to be designed, trained, and validated entirely in simulation before physical hardware exists, compressing development timelines and de-risking robotics programs across Amazon. The team delivers end-to-end simulation stacks for Amazon's robotics programs, including high-fidelity robot digital twins, teleoperation data collection infrastructure, scalable synthetic demonstration generation, policy training and inference pipelines (RL, imitation learning, VLAs), domain randomization for sim-to-real transfer, and model validation in simulation. We partner closely with hardware teams, science organizations, and robotics program leads across Amazon Robotics.

We are Amazon's central Responsible AI team. Our mission is to advance the science and practice of Responsible AI (RAI) to enable Amazon's tens of thousands of builders to build and deploy AI solutions to the high standards that our customers and society expect. As a scientist on this team, you will: - understand in depth the technical and scientific issues related to RAI, including controllability, security, privacy, safety, veracity, robustness, fairness, explainability, transparency, and governance - help define the strategies, priorities and metrics for RAI - solve open problems in RAI to unblock traditional, generative and/or agentic AI use cases and solutions, publishing as appropriate - help develop our team - liase with internal and external stakeholders, including the academic community, on issues related to RAI About the team 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.

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

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading 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. Amazon Ads Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Machine Learning Applied Scientist who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems. Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. We are looking for a talented Machine Learning Applied Scientist for our Amazon Ads Response Prediction team to grow the business. We are providing advanced real-time machine learning services to connect shoppers with right ads on all platforms and surfaces worldwide. Through the deep understanding of both shoppers and products, we help shoppers discover new products they love, be the most efficient way for advertisers to meet their customers, and helps Amazon continuously innovate on behalf of all customers. Key job responsibilities * 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 to optimize all aspects of Sponsored Products and Brands business