Careers

Employer: Amazon Web Services, Inc. Position: Applied Scientist II - AMZ27496.1 Location: Boston, MA 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. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. (40 hours / week, 8:00am-5:00pm, Salary Range $161803 - $193200) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000

At Amazon we believe that Every Day is still Day One! We’re working to be the most customer-centric company on earth and Amazon's Central Reliability Maintenance Engineering (C-RME) team is at the heart of that mission, using science and data to drive scalable maintenance best practices across Amazon business units globally. We are seeking a Senior Applied AI Engineer/Scientist to lead key semantic layer and knowledge intelligence initiatives. This role sits at the intersection of knowledge engineering, ontology design, and applied AI, owning workstreams for the development of semantic foundations and dedicated science approaches that ensure their accuracy, consistency, and explainability in service of agentic and non-agentic AI across RME. Key job responsibilities In this role, you will contribute to the success of Central and Field RME teams working with new launches of Amazon buildings, as well as ensure that our Field teams benefit from state-of-the art AI solutions to support Global Operational Excellence. You will closely work with our team of senior scientists and systems engineers in our knowledge intelligence team, which is leading the full lifecycle of graph-based AI solutions, from customer problem formulation and ontology design to production deployment, enabling network-wide data discovery, decision support, and compliance monitoring. A core part of your mandate is to lead the semantic modelling and ontological foundation layer that supports both explainability and retrieval capabilities. This foundation feeds into transversal initiatives spanning multiple teams and products involving multiple AI approaches. You will closely work with Senior Applied Scientists owning explainability, causal reasoning, intelligent retrieval and question answering over knowledge graphs. As a Senior Applied AI Engineer/Scientist, you will: • lead the semantic layer for agentic AI, including developing and assessing the ontological foundations that enable autonomous workflows and cross-site best practice sharing. You will also disseminate governance and standardization practices that ensure downstream consumers (including retrieval and explainability systems) operate on consistent, well-defined semantics • design, build, and deploy graph-based AI solutions that combine knowledge graphs, Large Language Models (LLMs), and ML models to extract meaning from large-scale unstructured document collections, enabling data discovery, classification, and governance across RME • define and own knowledge pipelines that extract, transform, and enrich entity relationships from diverse unstructured and semi-structured sources into production-grade knowledge graphs, ensuring reliability and accuracy of the overall information architecture • collaborate with fellow senior scientists to design, deploy, and operate graph and vector databases to support retrieval, causal reasoning, and analytics use case as well as ensure contributing scientists maintain versioning, validation state, and provenance for every knowledge graph entry • collaborate with fellow senior applied scientists to ensure ontology and schema design decisions optimize for queryability, so that question-answering and retrieval systems can leverage the semantic layer with minimal impedance mismatch • integrate LLMs and ML models into text processing pipelines for classification, embedding generation, document similarity, entity extraction, and semantic analysis, applying rigorous experimentation and evaluation methodology to select the best fit-for purpose approach • design ontological structures that support explainability, enabling agents and reasoning systems to trace reasoning paths and surface provenance, enabling governance-level transparency for autonomous AI actions • optimize models and inference pipelines for production constraints including latency, throughput, cost, and infrastructure reliability • establish best practices and standards for knowledge engineering and applied science processes, elevating the maturity of RME's data, information, and AI capabilities • mentor and train colleagues on knowledge graph concepts, semantic modelling, and applied AI techniques About the team The Amazon Reliability and Maintenance Engineering (RME) team maintains and optimizes technologies ranging from large, modern, purpose-built warehouses utilizing robotics and high-volume conveyance all the way through the value chain to small, high-speed warehouses placed as close to our customers as possible. Central Reliability Maintenance Engineering (RME) uses science and data to drive scalable maintenance best practices across Amazon business units globally. We do this to meet our customer promise, reduce costs, and support the Climate Pledge.

We are seeking an Applied Scientist to join the SAF Lab. In this role, you will lead the effort in safe reinforcement learning (RL) including the development of legged locomotion algorithms that internalize safety and are deployable on physical hardware—enabling highly dynamic robots to walk, run, avoid collisions and recover from disturbances with agility and robustness. You will develop RL architectures that interface with physics-based models (for dynamic retargeting and reward shaping), internalize safety constraints in training, sim-to-real transfer and interface with safety filters at run-time. Therefore, your work will sit at the intersection of safety-critical control and learning, and you will collaborate with others in the SAF Lab and Amazon working on perception, planning, whole-body and safety-critical control. This is an opportunity to shape the foundations of safe learning on emerging platforms that will remove bottlenecks to deployment and enable these robots to safely operate around humans. Key job responsibilities • Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots). • Design, train, and deploy reinforcement learning (RL) policies for dynamic legged locomotion including walking, running, stair climbing, and fall recovery on physical robots • Develop sim-to-real transfer pipelines that produce policies robust to the reality gap, including domain randomization, system identification, and adaptive strategies • Integrate control-based methods with RL, as inputs to the RL (dynamic retargeting and control-guided rewards), in training (internalizing safety constraints in training), and as the RL feeds into safety layers and whole-body control • Develop and maintain large-scale training infrastructure for locomotion policy learning, including physics simulation environments, domain randomization and GPU parallelization • Investigate the distillation of locomotion policies, integration with whole-body control, foundation models, VLAs, world models, perception and full-stack autonomy • Evaluate policy performance rigorously through simulation benchmarks, hardware experiments, and failure-mode analysis • Publish research at top-tier robotics and ML venues and contribute to Amazon's scientific reputation in advanced robotics • Collaborate with perception and planning teams to enable terrain-aware and goal-conditioned locomotion behaviors 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 Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, mobile manipulators, and future platforms with dynamic stability. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin robots operating alongside people at Amazon's unprecedented scale.

Are you passionate about helping customers achieve business transformation through AI? Do you want to lead forward-deployed teams that embed directly into the enterprise and unlock real business outcomes? And are you ready to operate as a general manager across engineering, science, and commercial strategy in the fastest-moving space in AI and infrastructure? The AWS Generative AI Innovation Center (GenAIIC) is on a mission to accelerate enterprise AI transformation across global customers going from beyond isolated use cases to holistic, C-suite-sponsored initiatives that reshape how organizations operate. We combine deep AI expertise across science, strategy, and business transformation. We start with the customer's most critical operational challenges and work backwards, and deploy multidisciplinary teams that embed with the customer, prove impact in 45-day sprints, and expand across the enterprise. We are a fast-moving, entrepreneurial team that values leaders who can operate across technical depth and commercial breadth. You will lead a team of ML engineers, AI scientists, and AI strategists who work alongside customers to architect and deliver AI solutions that move and stay in production, realizing value. You will regularly engage with CFOs, CIOs, and C-suite executives. You must bring equal fluency in engineering, data science, go-to-market, and customer delivery. You are ready to roll up your sleeves alongside the team, whether that means scoping an agentic AI architecture, presenting to a board, or operationalizing a repeatable delivery motion. You will partner with customers, AWS Sales, AWS service teams, AWS industry teams and AWS Professional Services delivery teams to meet the specific needs of the customer, and extend that use to other customers. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. 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. 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 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 in the cloud.

Trusted by more startups around the world, AWS makes the power of cloud computing accessible for all by giving founders everywhere access to the same technology that powers the world's largest companies. With nearly two decades of experience supporting hundreds of thousands of startups, including 80% of unicorns, we democratize cloud computing to help founders bring their innovative ideas to life. We support founders at every stage of their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Data is central to how we do this: it helps us identify high-potential startups early, personalize the guidance we deliver, and prioritize where we can create the most value for founders and for AWS. We are seeking an Applied Science Manager to lead a team of applied scientists and analysts building the data and machine learning capabilities behind AWS Startups. You will own the science roadmap end-to-end, from the data foundation that unifies signals about founders, startups, and their products, through a portfolio of machine learning models, to the surfaces that put insights in the hands of the teams and products that serve startups. You will balance hands-on technical leadership with people management, setting the technical bar for your team while developing their careers. Key job responsibilities · Lead, coach, and grow a team of applied scientists, business intelligence engineers, and business analysts; hire and develop talent and set a high technical bar. · Own and prioritize the team's science roadmap across data foundation, model development, and the delivery of insights into products and internal tools. · Set technical direction for the team's machine learning models and data assets, balancing rapid experimentation with production quality, cost, and reliability. · Scope scientific projects, design and evaluate experiments, and ensure models are productionized and deliver measurable business impact. · Establish measurement, evaluation, and operational-excellence standards so model quality and impact are quantified and defensible. · Partner with product, engineering, design, and go-to-market teams to translate science into products and repeatable, scalable outcomes. · Communicate strategy, results, and trade-offs clearly to technical and non-technical leaders through written narratives and business reviews. · Foster a culture of scientific rigor, rapid experimentation, and operational excellence, and proactively identify and escalate risks with clear mitigation plans. About the team The AWS Startups team builds innovative products and platforms that support startup customers throughout their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Our portfolio serves hundreds of thousands of startup customers globally, and we partner with business development, field marketing, and solutions architecture teams worldwide. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder.

The Advertising Demand Tech Organization is on a mission to make Amazon the best in class destination for shoppers to discover, engage and build affinity with brands, making shopping beautiful, delightful, and personal. We enable any advertiser – regardless of budget size, advertising experience, or technical expertise to set up display and streaming video campaigns to run on key placements across Amazon retail website and apps, Twitch, IMDB, Amazon Devices, Freevee, Prime Video and third party websites (e.g. nytimes.com) and mobile apps. You will lead a team within Performance & Ad Serving which owns, develops and operates robust services, datasets and machine learning models that are able to handle large volume of transactions and data and operating at a global scale. In this role you will lead a team that designs, prototypes, builds, delivers and operates live systems for Amazon’s business worldwide. You will have a track record building and managing teams that deliver on projects that require significant innovation. You will apply analysis and scientific rigor to build systems that surprise and delight our customers, using data to determine areas of highest impact, bring the latest scientific thinking from inside and outside Amazon to bear on the problem. Your team will develop fundamental new approaches. You will form a pillar between best-of-breed research thinking and real products and systems, with an opportunity to innovate and iterate at pace and scale. The ideal candidate will have proven experience leading a team that uses state-of-the-art machine learning for real-world problems that deliver meaningful business impact. Strong computer science and algorithmic skills, exposure to internet-scale businesses, working in cross-functional teams, and a track record of peer-reviewed publication in a relevant area are desirable. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. We are passionate about building scalable, well-designed software services, and strive to constantly improve our technical foundation and user experience. As an manager, you’ll have a direct impact on our customers by making it easy and efficient for these suppliers to advertise their brands and products, increase sales and improve the Amazon shopper experience. We’re working hard, having fun, and making history! Come join us!

The Amazon Smart Vehicles (ASV) science team is seeking a passionate and skilled Applied Scientist with extensive expertise in advanced LLM technologies. This role involves innovating in rapidly evolving areas of AI research, focusing on creating personalized services to enhance drivers' and passengers' experiences. Your work will aim to simplify their lives, keep them informed, entertained, productive, and safe on the road, with direct application to prominent Amazon products. If you have extensive expertise in LLMs, natural language processing, and machine learning, along with experience in high-performing research teams, this could be the perfect opportunity for you. Our dynamic and fast-paced environment demands a high level of independence in decision-making and the ability to drive ambitious research initiatives through to production. You will collaborate closely with other science and engineering teams, as well as business stakeholders, to ensure your contributions are both impactful and delivered with maximum efficiency. Key job responsibilities - Leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI) - Work with talented peers to lead the development of novel algorithms and modeling techniques to advance the state of the art with LLMs - Collaborate with other science and engineering teams as well as business stakeholders to maximize the velocity and impact of your contributions About the team This is an exciting moment to lead in AI research and application. As part of the Amazon Smart Vehicles science team, you have the opportunity to shape the future by enhancing information-driven experiences for Amazon customers around the globe. Your work will directly influence customers through innovative products and services powered by language and multimodal technology!

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge 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. The Marketplace Intelligence (MI) team is looking for an Applied Science Manager to lead a team of scientists and engineers in building production ML and bandit solutions to customize the search experience. We determine which ads to show in Amazon search, where to place them, how many ads to place, and to which customers. This helps shoppers discover new products while helping advertisers put their products in front of the right customers, aligning shoppers’, advertisers’, and Amazon’s interests. To do this, we apply a broad range of machine learning, causal inference, and optimization techniques to continuously explore, learn, and optimize the allocation and ranking of ads on the search page. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the SP experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. You’ll lead the MI Interleaving team. The Interleaving team’s mission is to personalize and contextualize SP ad allocation on the search page. We do this by modeling shopper responses to the number, placement, and quality of ads. We use online experimentation, simulation, causal modeling, and online feedback to estimate the cost of displacing organic and sponsored content. Then, we incorporate those estimates into ad allocation to deliver an efficient and customized shopping experience for shoppers and improved discoverability and sales for advertisers. You’ll own the experimentation systems, models, and online model serving infrastructure to support these solutions. This is a unique opportunity for someone who wants to have broad business impact, a direct impact on customers and the search experience, build scaled real-time LLM and ML solutions, and lead a cross-functional team. If you are interested in machine learning, bandit learning, building production systems, and leading a team to build these solutions, this role is for you. We’re looking for a leader who can help lay out the vision for the team and grow with it. Key job responsibilities * Lead a team of scientists and engineers in building scalable machine learning solutions. * Develop a vision for contextualizing and personalizing SP ads in Amazon search. * Create, develop, and drive a data-driven product strategy to define the right quantitative measures of shopper impact, using this to evaluate decisions and opportunities. * Tackle and solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. * Own a portfolio of pragmatic long-term investments that drive long-term growth of the ads and retail businesses. * Develop real-time LLM and ML algorithms to allocate billions of ads per day in advertising auctions. * Develop efficient algorithms for multi-objective optimization and AI control methods to find operating points for the ad marketplace then evolve them * Develop scientists and ML engineers around machine learning, economics, and optimization for Advertising.

Embark on an exciting science journey with International Emerging Stores Payments as we apply LLM techniques to improve the customer experience across multiple countries where we operate in. You will impact 1/4 of the world's population. Key job responsibilities 1. Translate ambiguous opportunities in to defined science problem statements 2. Evaluate and identify the right ML algorithm to solve for the problems. 3. Identify and build the right features for the Models 4. Partner with IML team in applying GenAI development strategies to accelerate model development 5. Publish papers to internal and external conferences.

Amazon Advertising drives billions of ad impressions and millions of clicks daily, powering discovery and sales for advertisers across Amazon's Retail and Marketplace businesses. The Ads Marketing Decision Science team sits at the intersection of data science and marketing strategy. We build intelligent, data-driven systems that analyze advertiser behavior at large scale to deliver the right guidance to the right advertiser at the right time. Our work spans behavioral modeling, content intelligence, automated decision systems, and GenAI applications, enabling personalized marketing experiences that help advertisers make smarter advertising decisions and grow their business on Amazon. We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models, design experiments, develop segmentation frameworks, and leverage GenAI capabilities where applicable, taking solutions end-to-end from proof-of-concept to production at scale. You will partner closely with scientists, engineers, and product managers on a daily basis to prototype rapidly, ensure data integrity in production systems, and deliver measurable advertiser impact. If you are passionate about solving real-world problems with next level science, come join us as we innovate and make history. Key job responsibilities • Define and execute data science solutions end-to-end, from problem framing through production deployment. • Build machine learning models (classification, regression, clustering, ranking) for advertiser segmentation, propensity modeling, and recommendations. • Apply causal inference and experimentation methods (A/B testing, difference-in-differences, propensity score matching) to measure the impact of marketing interventions. • Analyze large-scale advertiser behavioral data to identify trends, surface growth opportunities, and support optimal decision making. • Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale. • Establish and drive data hygiene best practices to ensure coherence and integrity of data feeding into production ML/AI solutions. • Leverage GenAI and LLM capabilities to enhance science products where applicable A day in the life You will solve real-world problems by analyzing large volumes of advertiser data, building predictive models, designing experiments, and measuring business impact. You will prototype rapidly, validate ideas with data, and partner with engineers to productize and scale successful solutions. You will collaborate daily with scientists, engineers, and product managers across the advertising organization, working in a cross-functional, fast-paced environment where data drives decisions and helps advertisers grow. About the team We are a team of Applied Scientists, Research Scientists, Data Scientists, and Business Intelligence Engineers with deep expertise in ML, NLP, Gen-AI, RL, and causal inference, from a diverse range of backgrounds. We partner closely with strong engineers, product managers, and sales leaders who bring ads-industry depth and experience building scalable modeling and software solutions.