Careers

The Shopping Convo Foundations Team - Pre-purchases Science is looking for an Applied Scientist with expertise in Artificial Intelligence and Machine Learning to drive scientific innovation that expands Amazon's product catalogue. Our goal is to leverage AI/ML solutions to enhance catalogue coverage with high precision. In this role, you will research and develop novel machine learning approaches to solve complex catalogue expansion and product attribute challenges. You will design and develop state-of-the-art ML models, conduct rigorous experimentation, and translate scientific breakthroughs into production-ready solutions. You will work closely with ML Engineers and Software Development Engineers to optimize model performance, ensure scalability, and deploy low-latency solutions at Amazon scale.

Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists

Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. We are seeking a talented Applied Scientist to join our advanced robotics team, focusing on developing and applying cutting-edge simulation methodologies for advanced robotics systems. This role centers on research and development of physics-based simulation techniques, sim-to-real transfer methods, and machine learning approaches that enable rapid development, testing, and validation of robotic systems operating in complex, real-world environments. Key job responsibilities - Advance physics-based simulation fidelity for contact-rich manipulation and locomotion - Design and build high-performance simulation tools integrated into a robotics design stack - Translate research ideas into robust, verifiable data - Develop methods to quantify and reduce simulation-to-reality gaps across design, safety, and control - Architect scalable simulation solutions for rigid and deformable body dynamics - Build simulation pipelines optimized for a digital twin level of fidelity - Establish frameworks for continuous simulation improvement using real-world hardware - Collaborate with engineering, science, and safety teams on simulation requirements and validation About the team Our team is building a comprehensive robot simulation and modeling platform for advanced robotics development, combining locomotion and manipulation capabilities. We operate at the cutting edge of physics simulation, reinforcement learning, hardware-in-the-loop (HIL), and sim-to-real transfer, collaborating with world-class robotics engineers, scientists, and mechanical designers in a fast-paced, innovation-driven environment. This role uniquely combines fundamental research with real-world development. You will pursue core research questions in physics-based simulation while seeing your work translated into real robots, validated on real hardware. Working alongside Robot scientist and designers, you will help transform research ideas into scalable, quantifiable simulation capabilities that directly impact how robots are designed and built.

Do you want to join a team of innovative scientists to research and develop generative AI technology that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment? Amazon Connect is a highly disruptive cloud-based contact center from AWS that enables businesses to deliver intelligent, engaging, dynamic, and personalized customer service experiences. The Agentic Customer Experience (ACX) org is responsible for weaving native-AI across the Connect application experiences delivered to end-customers, agents, and managers/supervisors. The Interactive AI Science team, serves as the cornerstone for AI innovation across Amazon Connect, functioning as the sole science team support high impact product including Amazon Q in Connect, Contact Lens and other key initiatives. As an Applied Scientist on our team, you will work closely with senior technical and business leaders from within the team and across AWS. You distill insight from huge data sets, conduct cutting edge research, foster ML models from conception to deployment. You have deep expertise in machine learning and deep learning broadly, and extensive domain knowledge in natural language processing, generative AI and LLM Agents evaluation and optimization, etc. You are comfortable with quickly prototyping and iterating your ideas to build robust ML models using technology such as PyTorch, Tensorflow and AWS Sagemaker. The ideal candidate has the ability to understand, implement, innovate on the state-of-the-art Agentic AI based systems. We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and scientific problems. We are looking for passionate, talented, and experienced people to join us to innovate on modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus. About the team 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.

As a Sr. Applied Scientist, you will be responsible for bringing new product designs through to manufacturing. You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will use expertise in physical sciences, theoretical, numerical or empirical techniques to create scalable models representing response of physical systems or devices, including: * Applying domain scientific expertise towards developing innovative analysis and tests to study viability of new materials, designs or processes * Working closely with engineering teams to drive validation, optimization and implementation of hardware design or software algorithmic solutions to improve product and customer risks * Establishing scalable, efficient, automated processes to handle large scale design and data analysis * Conducting research into use conditions, materials and analysis techniques * Tracking general business activity including device health in field and providing clear, compelling reports to management on a regular basis * Developing, implementing guidelines to continually optimize design processes * Using simulation tools like LS-DYNA, and Abaqus for analysis and optimization of product design * Using of programming languages like Python and Matlab for analytical/statistical analyses and automation * Demonstrating strong understanding across multiple physical science domains, e.g. structural, thermal, fluid dynamics, and materials * Developing, analyzing and testing structural solutions from concept design, feature development, product architecture, through system validation * Supporting product development and optimization through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques

The Measurement, Ad Tech, and Data Science (MADS) team at Amazon Ads is at the forefront of developing innovative solutions that help tens of millions of advertisers understand the value of their ad spend while prioritizing customer privacy and measurement quality. The Media Planning Science team develops and implements models that deliver insights and recommendations for strategic media planning and measurement across Amazon Advertising's product portfolio. Our mission is to help advertisers create and execute plans that meet their objectives while providing accurate measurement tools. We work on a multitude of problem statements that encompass Reach and Frequency, Budget Planning Optimization, and Recommendations. Our models leverage both heuristic and machine learning approaches including deep learning techniques, with insights delivered through agent-based tools and APIs that integrate seamlessly into user interfaces and programmatic systems to ensure optimal advertising outcomes. As an Applied Scientist on the team, you will be at the forefront of innovation, developing media planning solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities * Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement and media planning problems * Disambiguate problems to propose clear evaluation frameworks and success criteria * Work autonomously and write high quality technical documents * Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production * Partner closely with other scientists to deliver large, multi-faceted technical projects * Share and publish works with the broader scientific community through meetings and conferences * Communicate clearly to both technical and non-technical audiences * Contribute new ideas that shape the direction of the team's work * Mentor more junior scientists and participate in the hiring process A day in the life You will solve real-world problems by analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop ML/DL models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal documents to share insights to audiences of varying levels of technical sophistication. About the team We are a team of scientists across Applied, Research, and Data Science disciplines. You will work with colleagues with deep expertise in ML, DL, NLP, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

Amazon's AGI Information is seeking an exceptional Applied Scientist to drive science advancements in the Amazon Knowledge Graph team (AKG). AKG is re-inventing knowledge graphs for the LLM era, optimizing for LLM grounding. At the same time, AKG is innovating to utilize LLMs in the knowledge graph construction pipelines to overcome obstacles that traditional technologies could not overcome. As a member of the AKG IR team, you will have the opportunity to work on interesting problems with immediate customer impact. The team is addressing challenges in web-scale knowledge mining, fact verification, multilingual information retrieval, and agent memory operating over Graphs. You will also have the opportunity to work with scientists working on the other challenges, and with the engineering teams that deliver the science advancements to our customers. A successful candidate has a strong machine learning and agent background, is a master of state-of-the-art techniques, has a strong publication record, has a desire to push the envelope in one or more of the above areas, and has a track record of delivering to customers. The ideal candidate enjoys operating in dynamic environments, is self-motivated to take on new challenges, and enjoys working with customers, stakeholders, and engineering teams to deliver big customer impact, shipping solutions via rapid experimentation and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems. You will collaborate with applied scientists and engineers to develop novel algorithms and modeling techniques to build the knowledge graph that delivers fresh factual knowledge to our customers, and that automates the knowledge graph construction pipelines to scale to many billions of facts. Your first responsibility will be to solve entity resolution to enable conflating facts from multiple sources into a single graph entity for each real world entity. You will develop generic solutions that work fo all classes of data in AKG (e.g., people, places, movies, etc.), that cope with sparse, noisy data, that scale to hundreds of millions of entities, and that can handle streaming data. You will define a roadmap to make progress incrementally and you will insist on scientific rigor, leading by example.

Are you passionate about developing new state-of-the-art measurement approaches at Petabyte scale? Amazon Advertising is one of Amazon’s fastest growing businesses, and we are leveraging our unique data, the latest machine learning methods and big data technologies to better understand how Amazon’s marketing influences customer behavior. We are looking for a Senior Applied Scientist to develop new systems and methods in the most challenging and data rich areas of marketing. We need an expert in experimental statistics, machine learning or causal inference to design advanced new models with our world class data systems. Dozens of Amazon businesses use Amazon’s ad tech for their marketing objectives, driving more than $1B of marketing investments through Ads services and tools. As part of the 1PM team, this role will partner with a dedicated engineering team measuring the impact Amazon's marketing and identifying opportunities for optimization at scale. We drive initiatives to make smarter marketing decisions and improve the relevance of advertising to our customers. We move away from industry standard measurement systems and build sophisticated and insightful decision engines. We enable massive advertising programs, generating billions of impressions decorated with rich representations of customer state. The major challenges we are solving include integrating petabyte-scale distributed retail systems into a singular service to synthesize e-commerce data into measurement and optimization models. The successful candidate will have a causal inference background, a start-up mentality, an appreciation for white-space, and success solving problems with large data sets. Key job responsibilities • Scientists at Amazon are expected to develop new techniques to process large data sets and contribute to design of automated systems. • Apply ML, statistics or econometrics knowledge to develop and analyze prototype models. • Design and analyze data from large-scale online experiments in order to validate prototype models • Collaborate with scientists across teams in peer-review processes , publishing research in internal forms and industry conferences • Partner closely with product and engineering teams to develop new measurement systems and translate prototype models to production. • Establish scalable, efficient, and automated processes for large scale model development, validation, and implementation. • Research and experiment with novel statistical modeling approaches.

Join the AWS Perimeter Protection team as an Applied Scientist, where you will design and build AI/ML models that protect AWS customers from cyber threats at massive scale. You will work on challenging problems in threat detection, bot management, DDoS protection, and web application security — developing and deploying machine learning solutions that leverage techniques including large language models, generative AI, and agentic AI systems. Operating across all AWS regions and processing trillions of requests per week, you will collaborate with experienced scientists and engineers to deliver production-grade, intelligent security systems that provide robust, adaptive, and forward-looking protection for AWS customers worldwide. Key job responsibilities - Design, develop, and evaluate ML models and algorithms for threat detection, anomaly detection, and mitigation of evolving cyber threats including DDoS attacks, bot activity, and web application exploits. - Explore and apply large language models, generative AI, and agentic AI approaches to security challenges such as automated threat analysis, intelligent mitigation, and adaptive defense systems. - Implement end-to-end ML solutions — from data exploration and feature engineering through model training, evaluation, and deployment into production systems. - Analyze large-scale datasets to uncover patterns, identify emerging threat vectors, and translate findings into effective ML-based security solutions. - Build and maintain data pipelines and model training workflows that support rapid experimentation and reliable production performance. - Collaborate with software engineers to integrate ML models into low-latency, high-throughput security systems at cloud scale. - Design and run experiments to validate model performance, measure impact, and iterate on approaches using rigorous scientific methodology. - Stay current with recent advances in AI/ML — including LLMs, generative AI, and agentic systems — and cybersecurity research, applying relevant techniques to improve detection and protection capabilities. - Contribute to design reviews, and knowledge sharing. - Participate in the team's scientific roadmap by proposing ideas and identifying opportunities to improve existing systems.

Amazon’s Last Mile Delivery organization is responsible for the on-time and error-free delivery of tens of billions of packages annually, in 20+ countries worldwide. The organization’s focus over the years has expanded beyond package delivery to include groceries and heavy and bulky items and has dramatically increased the speed of delivery from two day, to one day, to sub-same day for millions of items, a trend that will continue with quick commerce deliveries within minutes. Underpinning this massive delivery logistics operation (one of the largest in the world) is innovative technology leveraging state of the art AI and ML solutions developed by the Geospatial Science team, one of the largest science teams within the Amazon Operations organization. The Geospatial Science team is responsible for the quality and coverage of the core geospatial data, solvers, and real-time workflows that operate over petabytes of data, power trillions of transit time calculations daily, and operate on diverse environments spanning multi-modal cloud-based learning workflows, highly throughput and low-latency services, and edge compute applications on smart phones, delivery vehicles, and delivery stations. Geospatial Science capabilities operate at the critical path a broad array of mission critical workflows ranging from customer address creation, order placement, delivery route planning, delivery route execution, and package drop-off. The Director, Applied Science (Geospatial) owns the end-to-end science portfolio that enables these capabilities by leveraging innovative AI and ML techniques. They are responsible for (1) learning and improving a worldwide catalog of addresses with high-quality validation and geo-resolution, (2) building a places dataset to model where we delivery ranging from every single single-family home, campus, building, and apartment - along with their relationships and delivery critical attributes such as delivery hours, access information, mail rooms, delivery lockers, parking locations, entrances, and drop-off geocodes, (3) developing maps that capture a fresh and accurate road network, enable precise transit paths that optimize travel times while reducing travel risk in delivery routes and on-road navigation experiences and (4) developing feedback loops that leverage edge capabilities of millions of smart phones and tens of thousands of delivery vehicles to capture fresh street imagery, learn street signs, road markings, and road obstructions at scale, and reconstruct key delivery events and activities to improve the fidelity of address, place, and road datasets, optimize routes, and reduce defects. This leader will lead a worldwide team of approximately 50 scientists, with expertise in generative AI, computer vision, and machine learning. This leader requires broad and deep skills in innovative AI and ML techniques to take advantage of the latest advances in the field. A key focus is accelerating the development and adoption of GenAI-based solutions, in the face of rapid shifts in the science and technology landscape, by guiding the team to maximize the value that can be delivered using latest LLMs, VLMs, agentic paradigms, and reasoning agents. Computer vision based solutions form an important part of the portfolio, as the team innovates on scaled inputs like satellite, aerial, and camera imagery for many problems, such as road learning and transporter safety. This leader will be expected to invest in research and innovation to deliver novel solutions to unlock new opportunities to grow the business while making pragmatic tradeoffs to deliver timely customer value, in conjunction with product, engineering, and operational leaders and teams. This leader will be expected to interface with senior leaders (up to SVP) and senior partners and stakeholders across the World-Wide Operations organization and Amazon. Day-to-day interactions will span product partners with whom s/he will design end-to-end customer solutions and long-term product plans and strategies, engineering partners with whom s/he will execute the development and productization of multi-modal workflows and solvers, and multi-disciplinary upstream and downstream stakeholders and partner teams. This leader will be expected to co-own yearly and 3-year planning documents for the Geospatial Technology space. S/he will also be expected to build and demonstrate advanced research prototypes and proposals up to the SVP level. S/he will be expected to recruit senior scientists and science leaders and managers for their own team as well as other peer teams across Amazon. S/he will need build and maintain a high-performing team and develop and promote scientists and science leaders (up to Principal/Sr Manager/Sr Principal). Key job responsibilities - Lead a worldwide team of scientists to develop and deploy AI and ML solutions for geospatial problems to accelerate and optimize Amazon's global delivery operations - Interface with senior stakeholders across engineering, product, and operations teams to design end-to-end solutions, execute model delivery to production, and drive shared goals - Contribute to strategic planning by developing yearly and 3-year planning documents - Present to senior executives (VPs) and stakeholders via demo sessions, science reports, and quarterly business reports - Drive innovation by leveraging SOTA scientific techniques ranging from GenAI (LLMs/VLMs/agents), computer vision, and traditional ML to solve delivery-related problems - Build organizational capability by recruiting and promoting senior scientists and science leaders and maintain a high-performing team