Careers

Work at the intersection of Generative AI, AI Agents, and large-scale ML, helping build Amazon's world-class advertising business. Key job responsibilities • Lead and contribute to end-to-end ML initiatives with high ambiguity, scale, and complexity from problem formulation through production deployment • Develop and optimize forecasting models by performing hands-on analysis of large-scale datasets to improve ad delivery prediction accuracy and operational efficiency • Build, experiment, and deploy machine learning models through rapid prototyping, rigorous experimentation, and close collaboration with software engineering teams for seamless productization. • Design and execute A/B experiments, collect performance data, and conduct statistical analysis to validate model impact • Establish scalable ML infrastructure including automated pipelines for data processing, model training, validation, and serving • Advance the state of the art by researching innovative machine learning techniques and applying them to forecasting challenges About the team You'll join a highly motivated, collaborative, and entrepreneurial team with a broad mandate to experiment, innovate, and break new ground. Here, your work will directly influence advertiser success and shape the future of programmatic advertising.

Interested to build the next generation Financial systems that can handle billions of dollars in transactions? Interested to build highly scalable next generation systems that could utilize Amazon Cloud? Massive data volume + complex business rules in a highly distributed and service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide. Key job responsibilities • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. A day in the life • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. About the team The FinAuto TFAW(theft, fraud, abuse, waste) team is part of FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conduct research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinate with Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

Are you passionate about building data-driven applied science solutions to drive the profitability of the business? Are you excited about solving complex real world problems? Do you have proven analytical capabilities, exceptional communication, project management skills, and the ability to multi-task and thrive in a fast-paced environment? Join us an Applied Scientist to deliver applied science solutions for Amazon Payment Products. Amazon Payment Products team creates and manages a global portfolio of payment products, including co-branded credit cards, installment financing, etc. Within this team, we are looking for an Applied Scientist who will be responsible for the following: Key job responsibilities As an Applied Scientist, you will be responsible for designing and deploying scalable ML, GenAI, Agentic AI solutions that will impact the payments of millions of customers and solve key customer experience issues. You will develop novel deep learning, LLM for task automation, text processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. As the Payment Products organization deals with problems that are directly related to payments of customers, the Applied Scientist role will impact the large product strategy, identify new business opportunities and provides strategic direction, which will be very exciting.

The Marketing Measurement & Performance Support (MAPS) organization is looking for a Science Manager, interested in leading a team of Economists, Data Scientists and Applied Scientists in designing a measurement system to solve one of the most challenging business problems in marketing measurement. This exceptional leader will develop solutions combining experimental evidence, observational models and decision frameworks to redefine brand marketing measurement. The MAPS organization’s mission is to be the most trusted source of measurement science solutions to drive marketing investment decisions across Amazon. The MAPS team provides incrementality, efficiency measurement services and decision support to marketing stakeholders across Amazon’s Stores suit of businesses. MAPS applies industry leading causal inference models and designs experiments to measure omni-channel effectiveness of marketing campaigns from these businesses worldwide. Our outputs shape Amazon product and marketing teams’ decisions and therefore how Amazon customers see, use, and value their experience with Amazon. As a Science Manager, you will lead a team of scientists to develop state-of-the-art models, while collaborating with other scientists, businesses, marketers, and software teams to solve key challenges facing the teams. Such challenges include measuring the incremental impact of multi-channel marketing portfolios, estimating the impact on long term inter-related customer actions, and scaling measurement solutions for WW marketplaces. Unlike many companies who buy existing off-the-shelf marketing measurement systems, we are responsible for studying, designing, and building systems to serve Amazon’s suite of businesses. Our team members have an opportunity to be on the forefront of marketing measurement thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, economists and software developers in the business. Key job responsibilities In this role, you will be a people manager and a technical leader in Econometric research with significant scope, impact, and high visibility. You will own developing the next generation of Causal Marketing-Mix-Media (MMM) models combining experimental evidence with observational econometric techniques. Your solution will deliver to business leaders accurate and actionable incrementality estimates and recommendations to optimize their marketing portfolio. As a successful Science Manager, you can navigate ambiguity, lead problem solving, guide development of new frameworks, and credibly interface between technical teams and business stakeholders. You are an innovator who can push the limits on what’s scientifically possible with a razor sharp focus on measurable business impact. You will coach and guide scientists in your team across different job families including Economists, Data Scientists and Applied Scientists to grow the team’s talent and scale the impact of your work.

Join us if you're excited about pushing the boundaries of what's possible in physical AI, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale. We are seeking an exceptional Principal Applied Scientist to drive technical innovation in visual reasoning foundation models. You will be a technical leader who sets the research direction, architects novel solutions, and delivers breakthrough results that advance the state of the art while solving real-world business problems. You will lead the efforts of building a next-generation visual reasoning engine powered by frontier Large Video Models (LVMs). Your mission is to build a system that rivals human understanding of the physical world—moving far beyond the static perception of detection and tracking into the realm of deep spatial-temporal reasoning. This is not a passive computer vision tool; it is an agentic collaborator capable of interpreting natural language instructions, navigating unstructured environments, and executing complex tasks. You will sit at the high-stakes intersection of LVMs, LLMs, and Agentic AI, engineering systems that don't just 'see' but reason and act within the physical world. You will own end-to-end technical solutions from research to production deployment, driving innovation through hands-on research, prototyping, and deployment while delivering production impact. Key job responsibilities - Direct the technical vision for next-gen visual reasoning, pioneering the use of LVMs to solve high-dimensional spatial-temporal problems - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data - Solve computational challenges to train foundation models at scale, taking advantage of latest developments in hardware and deep learning libraries - Architect scalable solutions that deliver real-time insights across diverse physical environments - Build agentic AI systems that autonomously execute end-to-end workflows, transforming visual data into actionable business intelligence - Collaborate with multiple science and engineering teams to build adaptations that power use cases across diverse domains - Publish research at top-tier conferences (CVPR, NeurIPS, ICML) and establish technical thought leadership in visual reasoning and multi-modal AI - Mentor scientists and engineers while maintaining significant hands-on contribution to technical solutions - Influence product roadmaps through deep technical expertise and business acumen A day in the life - 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 spatiotemporal reasoning to efficient multi-task learning - Guide and support fellow engineers in building scalable and reusable infrastructure to support model training, evaluation, and inference - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions with the team and key stakeholders - Conduct experiments and prototype new ideas - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. Join the next science and engineering revolution at AWS Applied AI Solutions, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of visual reasoning through advanced AI and foundation models. Our team builds cutting-edge visual reasoning systems powered by vision-language-reasoning models to understand complex behaviors in the physical world. Our algorithms process video streams to reason about people, objects, and activities in real-time—enabling automated understanding of physical environments at scale. We develop state-of-the-art foundation models that push the boundaries of spatiotemporal reasoning, moving far beyond static perception. We are building a visual reasoning foundation model that generalizes across diverse physical environments and domains. This represents a significant opportunity to develop frontier vision-language models, multi-modal AI, and agentic AI technologies that enable automated decision-making capabilities with massive business impact. We build everything end to end, from data curation to model training, evaluation, and inference, along with all the tooling needed to understand and analyze model performance.

Are you inspired by the power of Large Language Models (LLM) to transform the way we interact with technology? Are you fascinated by the use of Generative AI to build an advertiser facing solution that predict problems and coach users while they solve real word problems? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the customer service space? If so, Amazon Advertising's Support Product & Services (SP&S) team has an exciting opportunity for you as an Applied Scientist. Key job responsibilities • Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the advertising support center domain. • Use Transformers and apply other NLP techniques like Sentence embeddings, Dimensionality reduction, clustering and topic modeling to identify customer intents and utterances. • Use services like AWS Lex, AWS Bedrock etc. to develop advertising facing solutions • Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful solutions. • Automating feedback loops for algorithms in production. • Setup and monitor alarms to detect anomalous data patterns and perform root cause analyses to explain and address them. • Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences. A day in the life You will work closely with a cross functional team of Software Engineers, Product Owners, Data Scientists, and Contact Center experts. You will research and investigate the latest options in industry to apply machine learning and generative AI to real world problems. You will work backwards from customer problems and collaborate with stakeholders to determine how to scale new technology and integrate with complicated help channels used by advertisers everyday. About the team SP&S team provides solutions and libraries that are leveraged by teams all across Amazon Advertising to provide timely and personalized help. The team aims to predict Advertisers problems and proactively surface intelligent guidance to customers at the right time. As a AS, you will help the team to achieve its vision of building and implementing the next generation of Contact Center technology. You will build/leverage LLMs to train them on advertising support domain knowledge and work shoulder to shoulder with stakeholders to externalize to users in novel ways.

Join our Amazon Private Brands Selection Guidance organization in building science and tech solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon. The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon. As a Data Scientist you will investigate business problems using data, invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and prototypes. This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact. Key job responsibilities - Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions. - Perform data analysis and build data pipelines to drive business decisions. - Invent novel science solutions, develop prototypes, and deploy production software to solve business problems. - Review and guide science solutions across the team. - Publish and socialize your and the team's research across Amazon and external avenues as appropriate - Leverage industry best practices to establish repeatable applied science practices, principles & processes.

The Next Generation Developer Experience (NGDE) Science team is looking for an Applied Scientist who is passionate about building services and tools for developers that leverage artificial intelligence (AI) and machine learning (ML). You will be part of a team building AI-based services for Amazon Q Developer with the focus on redefining the way developer work. The team works in close collaboration with other AWS AI services such as AWS Bedrock, the AWS IDE Toolkit, and Amazon Sagemaker. If you are excited about working in cloud computing and building new AWS services, then we'd love to talk to you. Key job responsibilities As an Applied Scientist, you are recognized for your expertise, advise team members on a range of machine learning topics, and work closely with software engineers to drive the delivery of end-to-end modeling solutions. Your work focuses on ambiguous problem areas where the business problem or opportunity may not yet be defined. The problems that you take on require scientific breakthroughs. A day in the life Diverse Experiences AWS 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 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) and AmazeCon (gender diversity) 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. This team is part of AWS Utility Computing: Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. About the team The Amazon Web Services (AWS) Next Gen DevX (NGDE) team is building Amazon Q for Developers to re-imagine all aspects of how builders create applications. We build AI products that are deployed in the IDE, the AWS console, and in web-based tools and services. We provide developers with AI assistants to generate code, features, and entire applications as well as to interact with AWS. We explore new technologies and find creative solutions, produce original research and bring cutting edge innovation to our customers. Curiosity and an explorative mindset can find a place here to impact the life of engineers around the world. If you are excited about this space, this is the team for you.

Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!

Are you passionate about robotics and research? Do you want to solve real customer problems through innovative technology? Do you enjoy working on scalable research and projects in a collaborative team environment? Do you want to see your science solutions directly impact millions of customers worldwide? At Amazon, we hire the best minds in technology to innovate and build on behalf of our customers. Customer obsession is part of our company DNA, which has made us one of the world's most beloved brands. We’re looking for current PhD students with a passion for robotic research and applications to join us as Robotics Research Scientist II Intern/Co-ops in 2026 to shape the future of robotics and automation at an unprecedented scale across. For these positions, our Robotics teams at Amazon are looking for students with a specialization in one or more of the research areas in robotics such as: robotics, robotics manipulation (e.g., robot arm, grasping, dexterous manipulation, end of arm tools/end effector), autonomous mobile robots, mobile manipulation, movement, autonomous navigation, locomotion, motion/path planning, controls, perception, sensing, robot learning, artificial intelligence, machine learning, computer vision, large language models, human-robot interaction, robotics simulation, optimization, and more! We're looking for curious minds who think big and want to define tomorrow's technology. At Amazon, you'll grow into the high-impact engineer you know you can be, supported by a culture of learning and mentorship. Every day brings exciting new challenges and opportunities for personal growth. By applying to this role, you will be considered for Robotics Research Scientist II Intern/Co-op (2026) opportunities across various Robotics teams at Amazon with different robotics research focus, with internship positions available for multiple locations, durations (3 to 6+ months), and year-round start dates (winter, spring, summer, fall). Amazon intern and co-op roles follow the same internship structure. "Intern/Internship" wording refers to both interns and co-ops. Amazon internships across all seasons are full-time positions, and interns should expect to work in office, Monday-Friday, up to 40 hours per week typically between 8am-5pm. Specific team norms around working hours will be communicated by your manager. Interns should not have conflicts such as classes or other employment during the Amazon work-day. Applicants should have a minimum of one quarter/semester/trimester remaining in their studies after their internship concludes. The robotics internship join dates, length, location, and prospective team will be finalized at the time of any applicable job offers. In your application, you will be able to provide your preference of research interests, start dates, internship duration, and location. While your preference will be taken into consideration, we cannot guarantee that we can meet your selection based on several factors including but not limited to the internship availability and business needs of this role. About the team The Personal Robotics Group is pioneering intelligent robotic products that deliver meaningful customer experiences. We're the team behind Amazon Astro, and we're building the next generation of robotic systems that will redefine how customers interact with technology. Our work spans the full spectrum from advanced hardware design to sophisticated software and control systems, combining mechanical innovation, software engineering, dynamic systems modeling, and intelligent algorithms to create robots that are not just functional, but delightful. This is a unique opportunity to shape the future of personal robotics working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. Join us if you're passionate about creating the future of personal robotics, solving complex challenges at the intersection of hardware and software, and seeing your innovations deliver transformative customer experiences.