Careers

Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog (https://www.aboutamazon.com/news/transportation/amazon-prime-air-delivery-drone-reveal-photos). If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you. Come work on the Amazon Prime Air Team! Our Prime Air Drone Vehicle Design and Test team within Flight Sciences is looking for an engineer to help us rapidly configure, design, analyze, prototype, and test innovative drone vehicles. You’ll be responsible for developing, improving, and maintaining a suite of multi-disciplinary optimization (MDO) tools across all aircraft design disciplines. You’ll use these to explore new and novel drone vehicle conceptual designs in both focused and wide open design spaces, with the ultimate goal of meeting our customer requirements. You’ll have the opportunity to prototype vehicle designs and support wind tunnel and other testing of vehicle designs. You will directly support the Office of the Chief Program Engineer, and work closely across all vehicle subsystem teams to ensure integrated designs that meet performance, reliability, operability, manufacturing, and cost requirements. In addition, you’ll own the Flight Sciences assessments and analysis methods for the drone vehicle design as it progresses through later stages of development. About the team Our Flight Sciences Vehicle Design & Test organization includes teams that span the following disciplines: Aerodynamics, Performance, Stability & Control, Configuration & Spatial Integration, Loads, Structures, Mass Properties, Multi-disciplinary Optimization (MDO), Wind Tunnel Testing, Noise Testing, Flight Test Instrumentation, and Rapid Prototyping.

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As an Applied Scientist on our team, you will focus on building state-of-the-art ML models for biology. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams.

Amazon Live is building the future of shoppable video — connecting brands with customers through livestreams, short-form video, and creator-driven content across Amazon Shopping, Fire TV, Prime Video, and social platforms. The product serves millions of monthly viewers, processes billions of events daily across 9 marketplaces, and generates tens of millions of dollars in advertiser revenue through self-service and managed channels with a goal to reach 100MM+ MAUs and 20K+ brands in the next couple of years. Amazon Live has a mature data platform powering reporting across 9 marketplaces, 14+ dashboards, and real-time creator analytics. The opportunity ahead is different: proving the causal value of video to brands, Amazon's programmatic systems, and product decision-making. This requires production ML models, data experimentation frameworks, and content intelligence that do not exist today — and that is exactly what this role builds. You will be one of the first scientists on this team — defining the measurement methodology, experimentation standards, and model architecture from the ground up. The problems are high-ambiguity, the data is rich, and the impact is visible — your models will directly influence how brands invest and how millions of customers discover content. Are you excited by the challenge of building causal measurement, content intelligence, and ranking signals from scratch for a product customers interact with daily? Do you want to own the full lifecycle from research question through production deployment, where your work moves multi-million dollar business decisions? We are looking for an Applied Scientist to join the DESA team and build production-grade models, experiments, and signals that close these gaps. You will own problems end-to-end — from framing the research question through model deployment and A/B experimentation — working alongside Data Engineers who build the infrastructure and a BIE who owns executive reporting. Your outputs will not sit in notebooks. They will run in production, feed downstream ranking systems, power brand-facing metrics, and give partner teams the evidence they need to prioritize integrations with Amazon Live. Key job responsibilities - Design and deploy causal attribution models (incrementality testing, multi-touch) replacing heuristic approaches, producing defensible numbers for partner teams and brand-facing ROI metrics. - Build brand lifecycle models (LTV, cost-to-acquire, adoption funnel) and campaign optimization models (marketing mix, diminishing returns) that scale self-service revenue. - Design and run A/B experiments with proper methodology (holdouts, pre-registration, power analysis) for new product surfaces, ranking changes, and attribution model transitions. - Build multimodal and generative models for content intelligence — extracting structured signals from video and producing scored creative assets at scale. - Develop ranking and personalization features (content affinity, creator quality indices, cross-session engagement patterns) consumed by downstream distribution systems. - Build predictive models proving video value to Amazon's programmatic systems where existing retail signals fail. - Own the full lifecycle from research question through production deployment, monitoring, and iteration. - Present findings and methodology to senior leadership (Director/VP) and partner teams, translating model outputs into business decisions. - Contribute to the science community through internal publications, reading groups, and cross-team methodology sharing. We value builders who thrive in ambiguity, move fast from hypothesis to production, and measure their success by business outcomes rather than paper count. A day in the life You will partner directly with product managers, monetization leads, and engineering peers to scope what to measure and how to prove it. Some weeks you will be designing an incrementality framework for ad lift and presenting methodology to leadership. Other weeks you will be training a multimodal model on broadcast video, packaging ranking features for the distribution team, or developing an A/B experiment for a new video produce on a new discovery surface. You will rapidly prototype and test hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgment. You will work on DE-built foundational data assets (content metadata, shopper profiles, retail/advertising integrations) and have access to a self-serve analytics agents and agentic-operational tooling that accelerate exploration. You will present findings to senior leadership (Director/VP level) and partner teams, turning data into prioritization decisions. About the team The DESA team owns the data platform, analytics, and sciences for Amazon Live and Shop the Show. The team's mission: quantify the value of live video to Amazon's ecosystem and make that value programmable — for brands deciding where to invest, for product teams deciding what to build, and for Amazon's systems deciding what to show customers. You will be one of the first scientists on a team that has built the data foundation and is now ready to build the intelligence layer on top. The platform processes event data from internal Amazon systems and social surfaces across 9 marketplaces. DEs own the infrastructure and foundational data assets. The BIE owns executive reporting. Applied Scientists own the models, experiments, and signals that turn raw data into product value and business decisions. Your outputs become shared org infrastructure — attribution models for partner teams, ranking features for distribution, experiment methodology for product, and content intelligence for creative supply.

The Amazon Robotics team is seeking an experienced Applied Scientist to join our team. In this role you will apply the latest trends in research to solve real-world problems in robotics and AI. You will collaborate with a team of scientists and engineers building these applications. We holistically design, build, and deliver end-to-end robotic systems. Our team is also responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, machine learning scientists, software engineers, and hardware engineers to collaborate and deploy systems in the field. Key job responsibilities • Research, design, implement and evaluate complex perception, motion planning, and decision making algorithms integrating across multiple disciplines and leveraging machine learning. • Create experiments and prototype implementations of new learning algorithms and prediction techniques. • Work closely with software engineering team members to drive scalable, real-time implementations. • Collaborate with machine learning and robotic controls experts to implement and deploy algorithms, such as machine learning models. • Collaborate closely with hardware engineering team members on developing systems from prototyping to production level. • Represent Amazon in academia community through publications and scientific presentations. • Work with stakeholders across hardware, science, and operations teams to iterate on systems design and implementation. About the team Watch this video to learn more about Vulcan Pick team in Amazon Robotics: https://www.amazon.science/publications/vulcan-pick-a-robotic-system-for-picking-targeted-objects-from-fabric-pods

Team & Project Overview The NBS Data Central team powers analytics, data science, and AI capabilities for Worldwide Global Selling (WWGS). We build scalable data products, and insight-generation systems that drive seller growth across 10+ marketplaces. Seller Intelligence is a P0 foundation theme at the Global Selling level, formed by merging "One Tagging" and "Good Contact" workstreams. It provides seller identity, segmentation, and contact-reach infrastructure that underpins all downstream seller-facing AI workflows — including intelligent outreach, personalized recommendations, and automated engagement. Scope of Impact Own the science pillar for Seller Intelligence within a cross-functional POD (PM + DE + DS + SDE) Directly impact seller engagement metrics across CN, IN, LATAM, and East-Asia expansion regions Models and data products consumed by 5+ downstream teams (ESM, NSR, MKT, NBS AI Ops, ROC) Influence $100M+ annual seller GMS through improved segmentation and contact optimization Key job responsibilities Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage. Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling). Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions. Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals. Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis. Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership. Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools. Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM). Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.

We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning algorithms for motor control systems in robots. In this role, you will focus on creating and optimizing intelligent motor control strategies to enable robots to perform complex, whole-body tasks. Your contributions will be essential in advancing robotics by enabling fluid, reliable, and safe interactions between robots and their environments. Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation learning, or other advanced AI techniques to achieve natural, robust, and adaptive motor behaviors - Collaborate with multi-disciplinary teams to integrate motor control systems with robotic hardware, ensuring alignment with real-world constraints such as actuator dynamics and energy efficiency - Use simulation and real-world testing to refine and validate control algorithms - Stay updated on advancements in robotics, AI, and control systems to apply advanced techniques to robotic motion challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you. an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As a highly experienced and seasoned science leader, you will apply state of the art natural language processing and computer vision research to video centric digital media, while also responsible for creating and maintaining the best environment for applied science in order to recruit, retain and develop top talent. You will lead the research direction for a team of deeply talented applied scientists, creating the roadmaps for forward-looking research and communicate them effectively to senior leadership. You will also hire and develop applied scientists - growing the team to meet the evolving needs of our customers. About the team This team's mission is to deeply understand all content and empower all customers with relevant language options, innovative accessibility assists, and rich title-information across all their content-experiences on Prime Video. We create and publish content on-time that's meaningful, accurate, and accessible to every customer globally. We delight our customers by pushing the boundaries of content understanding and enrichment. Through inclusion and innovation, we do the most fulfilling work of our career.

Do you want to make a real difference to real people's lives? Want to design and build fair and explainable systems which automate recruitment processes across Amazon? Come and be part of a team that develops new machine learning (ML) technologies, which help Amazon scale for its customers by recruiting diverse teams. Join our Recommendations team within Intelligent Talent Acquisition (ITA) where you’ll build machine learning products that transform how job seekers find opportunities and recruiters discover talent. You’ll develop sophisticated recommendation systems powering both Amazon Jobs and internal hiring platforms, operating at global scale to match the right people with the right positions. Using techniques including representation learning, reinforcement learning, and probabilistic modeling, your work will directly improve efficiency for recruiters and help candidates find their ideal roles. This position offers the chance to solve complex problems with significant impact by creating systems that make Amazon’s entire hiring ecosystem more effective while collaborating with scientists across the organization. Key job responsibilities - Design and implement machine learning models that power recommendation systems for job seekers and recruiters, ensuring high performance, scalability, and reliability at global scale. Our ideal candidate has a strong scientific foundation and experience of statistical analysis and model building and has a passion for fairness and explainability in ML systems. - Collaborate with engineers, scientists, and product managers to define requirements, create solutions, and deliver products that improve the hiring experience. - Participate in the full software development lifecycle including scoping, design, coding, testing, documentation, deployment, and maintenance of recommendation systems and ML models. - Solve complex ML problems using optimal data structures and algorithms, making thoughtful trade-offs between efficiency and maintainability. - Stay current with scientific literature and develop novel approaches that address business challenges in talent acquisition. You will have the opportunity to provide feedback on scientific work across the organization helping the entire Intelligent Talent Acquisition organization improve. A day in the life You might spend the morning reviewing a colleague’s code for a new recommendation algorithm feature, then collaborate with product managers to refine requirements for an upcoming enhancement. After lunch, you’ll dive into model development, analyzing performance metrics from recent A/B tests and implementing improvements to the job-seeker recommendation pipeline. Throughout the day, you’ll participate in scientific discussions with peers across the organization, providing valuable feedback while continuing to refine your expertise. About the team The Recommendations team is a hybrid group of software engineers and applied scientists located in Edinburgh. We build tools that match people to jobs and jobs to people, optimizing experiences for both recruiters and candidates. Our work directly impacts Amazon’s ability to find and hire exceptional talent globally. The team maintains a collaborative environment with regular knowledge sharing and mentorship opportunities. We work closely with our product teams to understand business needs and develop innovative scientific solutions that improve hiring outcomes across both industry and student requisitions worldwide.

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.