Careers

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Key job responsibilities As a Sr. Applied Scientist, you will - Help shape the scientific direction of the organization by proposing state of the art modeling approaches, driving experimentation, and balancing scientific rigor with execution speed to deliver measurable customer impact. - Think strategically about the future of GenAI and multimodal AI, identifying opportunities to transform music understanding, curation, and engagement. - Stay at the forefront of advancements in GenAI, recommendation systems, and large-scale machine learning, driving adoption of new techniques where they create meaningful customer value. - Collaborate closely with engineers across Music Intelligence, Personalization, Search and other partner teams to support long-term product and CX goals. - Mentor applied scientists and engineers while actively contributing to the broader science and ML community across Amazon Music. - Produce clear and concise technical documentation outlining methodologies, design decisions, trade-offs, experiment results, and customer impact. - Invent and scale innovative AI/ML solutions for complex music intelligence, personalization, metadata quality, and content understanding problems. - Drive the design of scientifically sophisticated ML systems and platforms, contributing core technical innovation and providing organization-wide architectural guidance. - Define the long-term science vision and roadmap for Amazon Music AI initiatives, translating customer needs into actionable plans for science and engineering teams. - Partner closely with engineering and product teams to build and launch scalable AI solutions that improve music discovery, personalization, and customer experience. - Lead rigorous experimentation and data-driven evaluation, including large-scale A/B testing, to measure and optimize customer impact. - Communicate complex scientific concepts clearly to technical and business stakeholders, including senior leadership. - Mentor scientists and engineers, fostering a culture of innovation, technical excellence, and strong customer focus. About the team The Amazon Music – Catalog Team develops sophisticated models for understanding music across multiple dimensions: sonic, thematic, cultural, lyrical, etc. This team aims to unify this deep music knowledge that will power intelligent music experiences across Amazon Music. Ultimately, the goal of our team is to delivery a musically credible experience, which will help grow engagement across all customers, but also delight the fans!

If you have ever bought or sold anything on Amazon, you have touched Amazon Marketplace. Amazon’s Marketplace business is one of the largest in the world. We are now in 23 countries. We are growing fast, with customers in many more countries. Amazon’s platform is the engine that powers Amazon’s Marketplace businesses, and Sellers rely on this platform and our support to start selling on Amazon and to grow their business. Amazon Marketplace enables millions of Sellers worldwide to list hundreds of millions of products and manage orders for inventory across dozens of different categories and languages. While working with millions of Sellers worldwide, we constantly strive to improve the selection for Customers and the capabilities of our platform for Sellers. The Seller Fulfillment Services (SFS) team is looking for a motivated and innovative Applied Scientist with strong analytical skills and practical experience to join our science team. As a key member of the SFS science team, you will provide expertise that helps accelerate the business. You will build science solutions that will help us to provide our customers with the largest selection of merchants at the lowest, and the most reliable delivery service regardless of the seller. You will research, design and improve on the models that will impact Amazon’s customer directly. You will be working in a highly collaborative environment partnering with various science, product management, engineering, operations, finance, business intelligence and analytics teams to develop science models to solve business problems. You will need to understand the business requirements and translate them into complex analytical outputs. You will design tests to explain performance of the models from impact on customer and cost perspective. You will create ML models to capture features impacting performance. You should be comfortable building prototypes, testing and improving them given the feedback from the real time data. You should be able to present your model and findings to a various range of stakeholders. Looking for candidate with expertise in the areas of machine learning, operations research, and statistics. With expertise in applying theoretical models in an applied environment relying heavily on the latest advances in machine learning, optimization, stochastic modeling, and engineering. The candidate will be expected to work on numerous aspects, such as feature engineering, modeling, probabilistic modeling, hyper-parameter tuning, scalable inference methods and latent variable models. Challenges will involve dealing with very large data sets and requirements on throughput. Key job responsibilities - Design, implement, test, deploy, and maintain innovative science solutions to accelerate our business. - Create experiments and prototype implementations of new learning algorithms and prediction techniques - Collaborate with scientists, engineers, product managers, and stakeholders to design and implement software solutions for science problems - Use best practices to ensure a high standard of quality for all of the team deliverables

We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers 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.

Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 200 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. The Observability and Triage team is looking for an Applied Scientist for our London office experienced in generative AI and large models. This is a wide impact role working with development teams across the UK, India, and the US. This greenfield project will deliver features that reduce the operational load for internal Prime Video builders and for this, you will develop AI-driven solutions that automatically detect anomalies, identify root causes, recommend resolution paths and take action for operational incidents. We consume petabytes of data daily across multiple metric, log and data based events and you would be experimenting on how to shape the future through this data. You will have strong technical ability, excellent teamwork and communication skills, and a strong motivation to deliver customer value from your research. Our position offers opportunities to grow your technical and non-technical skills and make a global impact. Key job responsibilities - Design and develop machine learning and generative AI systems for automated incident triage, root cause analysis, and resolution recommendation at scale - Rapidly prototype and evaluate hypotheses in a high-ambiguity environment, leveraging both quantitative experimentation and domain expertise in operational systems - Build evaluation frameworks (including LLM-as-a-Judge approaches) to measure model accuracy across triage accuracy and root cause prediction - Collaborate with software engineering teams to integrate ML models into production observability systems serving hundreds of development teams - Communicate results and insights to both technical and non-technical audiences, including through publications, presentations, and written reports A day in the life On a typical day, you analyse patterns across thousands of operational incidents to improve an automated triage model, then design an experiment to test whether a new Generative-AI based approach better identifies root causes for complex multi-service incidents. Your internal customers are Prime Video development teams who rely on your solutions to reduce the time and effort spent responding to operational events. You will collaborate closely with software engineers, and operational stakeholders across the world to ensure your research translates into production systems that measurably remove customer impact. About the team Our team builds AI-powered observability and triage solutions for Prime Video development teams, consuming petabytes of data daily to automatically detect, diagnose, and recommend resolutions for operational incidents.

We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Design and build agents to guide advertisers in conversational and non-conversational experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest 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 Campaign Strategies team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for FinAuto. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, 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.

Amazon Rufus Experience Science is seeking a highly motivated Scientist who is passionate about building next-generation shopping experiences. In this role, you will help create conversational shopping journeys where customers can express any shopping need—discovering products, comparing options, finding inspiration, or resolving post-purchase issues. You will collaborate closely with a multidisciplinary team of scientists, engineers, product managers, and designers to deliver these experiences across multiple Rufus customer-facing features.
You will thrive in this role if you enjoy bringing latest research into everyday life—both for customers and for yourself. There’s nothing quite like realizing that a model you deployed yesterday is already improving your own shopping experience today. You will work side by side with scientists and engineers in a fast-paced environment, driving rapid model development and experimentation. You’ll also have access to Amazon’s rich datasets, AWS’s massive computational resources, and a network of world-class science and engineering leaders across the company. Key job responsibilities Execute the science vision and roadmap.

Develop data-driven solutions for the real-world, large scale problems.

Deliver and maintain software and models in the production environment.

Collaborate cross-functionally between product, design, and engineering.

Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? We are looking for a Data Scientist who will be responsible to develop cutting-edge scientific solutions to optimize our fulfillment strategy across multiple regions of the world (EU, JP, IN and more), to maximize our Customer Experience and minimize our cost and carbon footprint. You will partner with the worldwide scientific community to help design the optimal fulfillment strategy for Amazon. You will also collaborate with technical teams to develop optimization tools for network flow planning and execution systems. Finally, you will also work with business and operational stakeholders to influence their strategy and gather inputs to solve problems. To be successful in the role, you will need deep analytical skills and a strong scientific background. The role also requires excellent communication skills, and an ability to influence across business functions at different levels. You will work in a fast-paced environment that requires you to be detail-oriented and comfortable in working with technical, business and technical teams. Key job responsibilities - Design and develop mathematical models to optimize inventory placement and product flows. - Design and develop statistical and optimization models for planning Supply Chain under uncertainty. - Manage several, high impact projects simultaneously. - Consult and collaborate with business and technical stakeholders across multiple teams to define new opportunities to optimize our Supply Chain. - Communicate data-driven insights and recommendations to diverse senior stakeholders through technical and/or business papers. - Leverage LLMs to improve explainability of our optimization solutions and drive engagement from supply chain planners across the world.

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! We are looking for an Applied Science Manager who will take care of the Content Understanding for Prime Video. Content Understanding team helps Prime Video "see" and "understand" what's actually happening inside its videos - the characters, scenes, dialogue, events, and visual elements. We build the systems that analyze video content so that we can build customer experiences on top of our work. This manager will also lead a team of scientists and engineers who are building the next generation of video understanding and search for Prime Video. You will own the strategy, execution, and delivery of systems that help machines watch, describe, and find content across the largest streaming catalog in the world. Key job responsibilities As an Applied Science Manager, you will: - Define and drive the technical direction and roadmap across our "understanding" and "search" workstreams — making sure our work connects to Prime Video's broader goals around content intelligence. - Lead and grow a high-performing team of scientists and engineers, building a culture of scientific excellence, customer focus, and reliable delivery. - Partner with teams across Prime Video (personalization, search etc) to understand what they need from us, shape our outputs to be reliable building blocks for their products, and drive adoption. - Own delivery end-to-end — from early research and experimentation all the way through launching production systems that run at the scale of Prime Video's full catalog. - Define and track success metrics for quality, reliability, and real-world impact on downstream products, continuously raising the bar on what "good" looks like. - Manage ambiguity across a broad set of workstreams, make clear prioritization decisions, and communicate trade-offs effectively to senior leadership. About the team The Prime Video - Content Localization, Understanding and Enrichment team's mission is to deeply understand content to automate & scale existing solutions, and launch new experiences across Prime Video while accelerating science outputs & forward-investing in science. This manager will lead Content Understanding that defines content at a fundamental scene-level by generating & maintaining a comprehensive set of Content Understanding attributes which are leveraged across Prime Video for their varied use-cases; ranging from content moderation to metadata generation, ad placement identification, etc.

At Amazon Robotics, we design advanced robotic systems capable of intelligent perception, learning, and action alongside humans, at massive scale. Our mission is to deploy robots that increase productivity and efficiency across Amazon fulfillment centers while operating safely and robustly in complex, contact-rich environments. We are seeking an Applied Scientist to develop manipulation controllers for robotic systems operating in contact-rich, uncertain environments. In this role, you will design force-aware control strategies grounded in impedance/admittance frameworks and augment them with data-driven policy learning to achieve robust, adaptive manipulation behaviors. You will combine physics-based modeling, control-theoretic design, and machine learning to build manipulation capabilities that generalize across objects, tasks, and operational conditions. You will collaborate closely with experts in perception, machine learning, motion planning, controls, and software engineering to deliver solutions that perform reliably on real hardware at production scale. As part of this role, you will study and extend relevant academic and industry research in robot learning and manipulation, prototype and validate learned policies in simulation and on hardware, and transition successful approaches into production systems. Successful candidates demonstrate strong intuition for physical systems, experience applying ML to robotics problems, and the ability to reason about failure modes, edge cases, and deployment constraints in contact-rich manipulation. Clear communication, hands-on experimentation, and a bias toward practical impact are essential. Key job responsibilities - Research, design, implement, and evaluate machine learning–based manipulation policies for contact-rich tasks, integrating learning with feedback control, estimation, and motion planning. - Develop learning frameworks that leverage simulation, real-world data, and hybrid physics- and data-driven models to enable robust agency interaction, grasping, insertion, and object handling. - Design and execute experiments in simulation and on hardware to train, validate, and stress-test learned manipulation policies under real-world variability and uncertainty. - Collaborate with software engineering teams to deliver scalable, real-time, and maintainable implementations of learning-based manipulation algorithms in production robotic systems. - Partner with cross-functional teams across perception, hardware, systems engineering, science, and operations to transition learned policies from research prototypes to reliable, production-ready capabilities across Amazon Robotics platforms. 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!