Careers

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.

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

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 seeking an exceptional Applied Scientist to join our JP Seller Services team, where you will reimagine how science analysis and modeling are conducted across the organization through an AI-native approach. In this role, you will design and build intelligent systems that enable any team member to validate business hypotheses with scientific rigor in hours rather than months. You will architect production-grade platforms spanning multi-agent AI frameworks, causal inference automation, generative AI, and simulation engines that democratize advanced analytics at scale. Your work will fundamentally transform how the teams generate, test, and deploy data-driven recommendations, scaling rigorous science solutions for every decision-maker to solve customer problems. The ideal candidate combines deep expertise in scientific analysis such as causal inference, machine learning, and AI system design with the vision to rethink the entire science lifecycle from hypothesis to deployment. At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture. Key job responsibilities - Lead the design and development of AI-native science platforms that automate the end-to-end lifecycle from hypothesis formulation through causal analysis, model validation, and deployment into production systems. - Design and build shared knowledge infrastructure (feature stores, experiment registries, model leaderboards) that enables cumulative organizational learning, where every validated insight accelerates future analyses. - Design and implement evaluation frameworks, including Seller simulations, that enable teams to validate model quality and test interventions against synthetic populations before live deployment. - Drive integration with downstream systems to close the gap between validated insights and seller-facing actions, ensuring science outputs reach the people and systems that serve customers. - Collaborate with cross-functional partners (product managers, category leaders, marketing managers, economists, and data scientists) to identify high-impact business problems and translate them into scalable scientific solutions.

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

Are you passionate about using data science to transform how businesses understand and optimize customer interactions at scale? Do you want to build the models and analytics that power the next generation of AI-driven customer experiences while working directly with customers to accelerate production deployments? As a Senior Applied Scientist within the Applied AI Solutions team, you will collaborate across AI Velocity Teams (AIVT), enabling multiple customer engagements simultaneously. You will lead data science initiatives that span the full lifecycle — from identifying high-value business problems and formulating hypotheses, through rigorous experimentation and modeling, to deploying production-grade solutions that serve thousands of customers. You will bring deep expertise in statistical inference, machine learning, and experimental design to drive measurable impact across Amazon Connect's analytics products and broader Connect AI initiatives. A critical dimension of this role is working directly with customers during production pilots to accelerate time-to-value. You will partner with Applied AI Solutions Architects and Customer Success Specialists to design, build, and deploy AI solutions in customer environments during fixed deployment cycles. You will enable field teams with data-driven insights, reusable analytical assets, ROI tools, and scalable tooling that accelerate customer engagements and solution delivery. Your work will directly influence customer decisions to adopt Connect Customer AI by quantifying business outcomes and demonstrating measurable value. You will operate with significant autonomy, owning the scientific direction of your projects while collaborating with applied scientists, software engineers, product managers, technical, and business stakeholders. You will be expected to identify the right methodology for each problem — whether that's a classical statistical approach, a modern deep learning technique, or a novel combination — and communicate your findings clearly to both technical and non-technical audiences. This role spans Connect AI initiatives including conversational analytics and agentic AI capabilities, offering the opportunity to pioneer data science approaches that scale intelligent analytics worldwide. Key job responsibilities - Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements and business decisions - Work directly with customers during production pilots to design, build, and deploy AI solutions that demonstrate measurable business value - Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes on customer outcomes - Build ROI models and business case tools that quantify the value of Connect Customer AI for existing customers transitioning from Connect Customer Basic - Develop and maintain forecasting systems for demand prediction, capacity planning, and workforce optimization - Develop and apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale - Partner with applied scientists and software engineers to productionize models, ensuring reliability, monitoring, and operational excellence - Enable AI Velocity teams with reusable analytical assets, diagnostic notebooks, and scalable tooling that accelerate customer engagements - Build benchmarking studies and optimization frameworks that demonstrate value across customer cohorts - Own success metrics and create mechanisms to measure model performance, adoption, and business impact - Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations - Operate as a shared resource across 2-3 AIVT teams simultaneously, providing data science expertise across multiple customer engagements A day in the life - Start the morning on a call with the AI Velocity Teams preparing for a strategic customer engagement — reviewing the analytical assets and dashboards you've built, walking through how to interpret model outputs, and tailoring recommendations to the customer's contact center environment - Join a customer working session where you're deploying a production pilot — analyzing their historical contact data, building demand forecasting models, and demonstrating how AI optimizations will reduce their cost per serviced contact while improving customer experience metrics - Dive into a deep analysis triggered by AIVT field feedback — a large enterprise customer is seeing unexpected patterns in their contact data, and you're pulling together multi-source data to isolate root cause and build a reusable diagnostic notebook the AIVT team can leverage for similar cases - Participate in a Conversational Analtyics science review, presenting your A/B test results on a new sentiment classification approach and discussing trade-offs between model accuracy and inference latency with the engineering team - Spend the afternoon building a reusable ROI calculator that field teams can use across customer engagements — packaging your economic models with configurable parameters so teams can quickly quantify the value of Connect Customer AI for different customer profiles and usage patterns - Collaborate with AI Architects and Customer Success Specialists across your three active AIVT engagements, providing data science guidance on model selection, evaluation frameworks, and success metrics for each customer's unique use cases - Wrap up by reviewing a design document for an agentic AI feature that will use conversation analytics to automatically surface coaching recommendations for contact center supervisors, providing feedback on the evaluation methodology and success metrics About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models. We are seeking an exceptional Senior Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases. Key job responsibilities - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. - Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. - As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. - Guide technical direction for specific research initiatives, ensuring robust performance in production environments. - Mentor fellow scientists while maintaining strong individual technical contributions. A day in the life As a member of the Delivery Foundation Model team, you’ll spend your day on the following: - Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure - Guide and support fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning - Guide and support fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems- Drive technical discussions within the team and and key stakeholders - Conduct experiments and prototype new ideas - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance. Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.

Amazon Robotics is revolutionizing warehouse automation at unprecedented scale. We are looking for a technical lead to own the whole-body planning, control, and optimization stack for the Phoenix mobile manipulation robot. In this role, you will design and ship motion planners and controllers that coordinate all of Phoenix's degrees of freedom (mobile base, torso, arm, and end-effector) as a single unified system, enabling smooth, safe, and production-ready autonomous manipulation in Amazon fulfillment environments. You will architect whole-body motion planning pipelines (task-and-motion planning, trajectory optimization) that generate collision-free, time-efficient trajectories across the full kinematic chain. You will develop and tune real-time whole-body controllers, including QP-based and model-predictive approaches, and integrate them with the Foundry Controller mainline. A key part of the role is ensuring planner-generated trajectories are high-quality enough to train downstream foundation model policies (e.g., π0.5) at scale. You will also extend Phoenix's Control Barrier Function (CBF) safety architecture, guaranteeing constraint satisfaction from planning through execution. About the team Amazon Robotics develops and deploys advanced robotic systems that power Amazon's fulfillment centers worldwide. Our innovations in mobile manipulation, autonomous navigation, and human-robot collaboration are transforming how products move through our network, enabling faster delivery times while creating safer, more ergonomic work environments for our associates.

Are you excited to play a strategic role in defining the customer experience on Tablets? Come join a dynamic, customer-obsessed, world-class science team to completely reshape the customer experience on our tablets. Key job responsibilities Using Amazon’s large-scale computing resources, you will design and deploy state-of-the-art recommendation models. You will ask research questions about customer behavior, design state-of-the-art models that help customers discover content, and deploy these models to production alongside other engineers. You will participate in the Amazon ML community and mentor other engineers with a strong interest in and knowledge of ML and generative AI. Your work will directly benefit customers! A day in the life We are looking for a passionate, hard-working, and talented Applied Scientist who has experience developing state-of-the-art models and deploying them to production. You will have an opportunity to make an enormous impact on the design, architecture, and implementation of products used everyday by real customers! About the team The Amazon Tablet team is a world-class team that delivers high-quality, innovative products that are loved by our customers. Our team focuses on spinning many of Amazon’s flywheels with bar-raising mobile experiences on our tablets. Over the last 12 years, we have built 4.0+ star rated products, and we continue to look for ways to scale our business. Join us as we continue our ambitious journey which will give you the opportunity to deliver disruptive products with global impact.

Employer: Amazon Development Center U.S., Inc. Position: Applied Scientist III - AMZ27579.1 Location: New York, NY Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. Telecommuting may be permitted. (40 hours / week, 8:00am-5:00pm, Salary Range $183800 - $248700) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation