Careers

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through 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. This position will be part of the Conversational Ad Experiences team within the Amazon Advertising organization. Our cross-functional team focuses on designing, developing and launching innovative ad experiences delivered to shoppers in conversational contexts. We utilize leading-edge engineering and science technologies in generative AI to help shoppers discover new products and brands through intuitive, conversational, multi-turn interfaces. We also empower advertisers to reach shoppers, using their own voice to explain and demonstrate how their products meet shoppers' needs. We collaborate with various teams across multiple Amazon organizations to push the boundary of what's possible in these fields. We are seeking a science leader for our team within the Sponsored Products & Brands organization. You'll be working with talented scientists, engineers, and product managers to innovate on behalf of our customers. An ideal candidate is able to navigate through ambiguous requirements, working with various partner teams, and has experience in generative AI, large language models (LLMs), information retrieval, and ads recommendation systems. Using a combination of generative AI and online experimentation, our scientists develop insights and optimizations that enable the monetization of Amazon properties while enhancing the experience of hundreds of millions of Amazon shoppers worldwide. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities - Serve as a tech lead for defining the science roadmap for multiple projects in the conversational ad experiences space powered by LLMs. - Build POCs, optimize and deploy models into production, run experiments, perform deep dives on experiment data to gather actionable learnings and communicate them to senior leadership - Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production. - Work closely with product managers to contribute to our mission, and proactively identify opportunities where science can help improve customer experience - Research new machine learning approaches to drive continued scientific innovation - Be a member of the Amazon-wide machine learning community, participating in internal and external meetups, hackathons and conferences - Help attract and recruit technical talent, mentor scientists and engineers in the team

We are seeking an Applied Science Manager to join the SAF Lab. In this role, you will lead a team of world-class applied scientists, engineers, post-docs and interns developing the next generation of safe autonomy on highly dynamic robotic systems. You will drive technical vision, research strategy, and ensure your team's innovations translate into production systems that operate at Amazon scale. You will interface with top academic researchers at the forefront of safe autonomy, and interdisciplinary teams across Amazon working on autonomous mobile robots, mobile manipulators, and dynamically stable robots. You will bridge academic research with real-world deployable safety layers that enable robots to safely operate around humans. Key job responsibilities • Manage a team of scientists and engineers developing a universal safety layer for robotic systems, with a focus on the next generation of robots • Work with leadership to design and execute multi-year research roadmaps for the development of safe autonomy that spans all types of current and emerging robotic platforms • Lead Scientists, Engineers, post-docs and interns to realize research and development goals and provide evidence of these results in a variety of formats • Drive integration of control barrier functions with planning, perception and learning while maintaining guarantees of safe high-performance robot behavior • Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots). • Publish research findings at top-tier conferences and contribute to the broader robotics community • Build relationships with academic and industry partners to stay at the forefront of safe autonomy 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.

We are seeking a Research Scientist to join the SAF Lab. In this role, you will develop the core Control Barrier Function (CBF) theory and algorithms that form the mathematical foundation of the universal safety layer. Key to this process is a feedback loop between theory and practice: developing theory that is deployed on next generation robots and using experimental evaluation to drive new theory. This will enable you to push the boundaries of CBF theory: layered safety filters and trade-offs between robustness and optimality. A key challenge will be to understand the interplay with CBF theory and learned control policies, constructing safety filters that internalize learned policies and utilizing CBFs in learning to internalize safety. You will work with the inventor of control barrier functions and a team contributing directly to the next generation of CBF theory and its practical deployment across Amazon's diverse robot fleet. Key job responsibilities • Develop and implement novel CBF algorithms that provide formal safety guarantees while minimizing conservatism to maximize the permissible operating envelope highly dynamic robots • Frame safety filtering within complex layered architectures involving learning-based components, including VLAs, RL-based locomotion and whole-body controllers • Design multi-layer CBF based safety filters, including decision making layers, MPC, and real-time nonlinear feedback control elements • Formalize the interplay between models used in the CBF safety filter and the full order dynamics of the robotic systems, establishing formal guarantees even if the full order system dynamics is not known and contains learning-based elements • Understand the role of perception and semantic representations in the synthesis of CBFs, and the interplay between CBFs • Characterize the trade-offs between optimal safety and robustness to sensor noise, perception error, actuator and sensor failure • Address the theory-to-practice gap by developing CBF methods that are robust to model uncertainty, sensor noise, actuation delays, and computational latency • Implement real-time optimization solvers (e.g., QP-based safety filters) that execute within the tight timing budgets of safety-critical control loops • Validate algorithms through rigorous simulation and hardware experiments, characterizing failure modes and quantifying safety margins • Contribute to the theoretical foundations of CBFs through publications at top-tier controls and robotics venues • Collaborate with perception, planning, locomotion, and manipulation teams to ensure CBF formulations accommodate the needs of upstream and downstream systems • Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots) 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, quadrupeds, and humanoids. 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 Amazon's path to millions of robots operating alongside people.

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.

Amazon Web Services (AWS) Applied AI Solutions (AAIS) is on a mission to make AI real for enterprises. We build and deploy production AI solutions that drive measurable business outcomes at scale, bringing together applied scientists, AI architects, business development professionals, and GTM specialists to help customers move from AI experimentation to production impact. Within AAIS, the GTM Acceleration team activates the field, measures impact, and scales what works. We are the connective tissue between AAIS product and science teams and the worldwide field organization, ensuring our AI solutions reach customers effectively, that we quantify the value we deliver, and that we build repeatable motions that scale globally. We are looking for an Applied Scientist who will serve as a force multiplier across our customer engagement teams, building the analytical foundations, predictive models, and reusable tooling that power our go-to-market strategy. You will work at the intersection of data science, machine learning, and business strategy, building models that quantify our value proposition, and creating scalable analytical assets that accelerate every engagement. This is a highly visible, high-impact role where your work directly influences how we demonstrate and measure the value of AWS AI solutions for enterprise customers. You will operate with significant autonomy, owning the scientific direction of your projects while collaborating with software engineers, product managers, and business stakeholders. You will identify the right methodology for each problem, whether that is a classical statistical approach, a modern deep learning technique, or a novel combination, and communicate your findings clearly to both technical and non-technical audiences. This role spans Connect Customer initiatives and across the Applied AI solution portfolio, offering the opportunity to pioneer data science approaches that scale intelligent analytics worldwide. If you thrive at the intersection of rigorous science and customer-facing impact and are energized by translating complex model outputs into business decisions, we want to talk to you. Key job responsibilities Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements, business decisions, and customer outcomes Work directly with customers during production pilots to build and deploy AI solutions that demonstrate measurable business value Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes Build ROI models, business case tools, and forecasting systems for demand prediction, capacity planning, workforce optimization, and value quantification Apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale, and partner with software engineers to productionize models with reliability, monitoring, and operational excellence Build and own customer analytics capabilities including segmentation (by size tier, AI adoption, product penetration, entitlement), usage trend analysis, propensity modeling, and foundational datasets combining service usage with sales data Create self-service analytics platforms and automated insight delivery mechanisms that enable leadership to pull strategic intelligence on demand Enable field teams with reusable analytical assets, diagnostic notebooks, benchmarking studies, and scalable tooling that accelerate customer engagements Own success metrics and create mechanisms to measure model performance, adoption, and business impact across customer cohorts Define strategic frameworks and GTM recommendations by segment, translating data patterns and market signals into actionable go-to-market motions and investment priorities Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations, operating as a shared resource across 2-3 teams simultaneously About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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 AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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.

Amazon Braket is investing in fault-tolerant quantum computing capabilities. We are looking for a Senior Applied Scientist with deep expertise in quantum error correction to work on compilation science as part of a team of scientists and engineers building fault-tolerant quantum capabilities. In this role, you will make design choices that directly influence production systems, working alongside the FTQC Science Lead to translate research direction into implementable solutions: which error correction approaches to pursue, how to map logical circuits to physical qubits, how to optimize resource usage, and how to integrate decoders into execution flows. You will work at the boundary of science and engineering, where your research directly informs what gets built. This is not a purely theoretical role. You will implement your ideas, benchmark them against real hardware constraints, and iterate with software engineers who translate your designs into scalable infrastructure. We are particularly interested in candidates who have taken QEC research from theory into implementation, whether in simulation or on physical hardware. Key job responsibilities - Drive scientific design decisions for fault-tolerant quantum workloads: error correction code selection, logical gate synthesis, and qubit mapping strategies - Develop and implement resource estimation algorithms that guide compilation optimization - Collaborate with software engineers to translate QEC research into production software - Benchmark approaches against realistic hardware noise models and device constraints - Work with quantum hardware providers on compilation strategies tailored to specific architectures - Publish research in coordination with the broader Braket science team, representing Amazon Braket at relevant conferences and workshops

Amazon Braket is investing in fault-tolerant quantum computing capabilities. We are looking for an Applied Scientist to own resource estimation and workload benchmarking for fault-tolerant quantum workloads on AWS. You will answer the fundamental questions: how many physical qubits are needed, what gate depths are achievable, and what error budgets are realistic for a given algorithm on a given device. Your models will inform technical decisions, customer conversations, and our roadmap. This role requires more than resource estimation methodology alone. You need a broad foundation in quantum error correction research to reason about the full picture: how code choices affect resource requirements, how logical circuit structure impacts physical costs, and how benchmarking results feed back into the system. You will be part of a small team of scientists and engineers, and we expect you to codify your solutions in production-quality code and contribute directly to the codebase alongside your teammates. Key job responsibilities - Build and maintain FTQC resource estimation models that determine qubit counts, gate depths, and error budgets for target algorithms - Develop benchmarking frameworks that evaluate compilation quality against realistic hardware constraints - Produce resource estimates that inform technical decisions and feed into customer readiness work led by the Applications & Engagement team - Collaborate with the QEC compilation scientists on how resource estimates feed back into code selection and optimization - Connect benchmarking outputs to published materials in coordination with Braket's science and product teams - Stay current with the rapidly evolving QEC literature and incorporate new results into estimation models

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 from Originals and Exclusive 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 240 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. Prime Video Commerce's mission is to present the right offer to the right customer at the right time — across subscriptions, channels, and transactional video in every market and on every device. Our science team replaces static business rules with ML-driven decisions that personalise the entire commerce journey, from discovery through to checkout and beyond. We operate at scale across hundreds of millions of customers, and we are now expanding into new frontiers — combining the latest advances in agentic and generative AI, behavioural simulation, and causal inference to understand the impact of our decisions before they reach customers. We are looking for an Applied Scientist to join the Prime Video Commerce Insights team who will work on the latest research and machine learning to build scalable personalisation solutions. You will develop and deploy customer-facing models, understand customer behaviour at scale, and explore emerging techniques that help us make better decisions faster. This is a hands-on role working with a high performing and high visibility multidisciplinary group of engineers and scientists in the London office, focused on improving the customer experience for Prime Video and the wider Amazon organization. You will contribute to the design of machine learning models that scale to large quantities of data and serve low-latency recommendations to all customers worldwide. You will embody scientific rigor in designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science and engineering team that embodies the customer obsession principle by developing recommendation and decision systems that raise the profile of Prime Video Commerce as a global leader in machine learning and personalisation. Successful candidates will have strong technical ability, a focus on customers by applying a customer-first approach, and excellent teamwork and communication skills. The position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. Key job responsibilities - Research, design, and implement recommendation systems that personalise across different customer experience touch points. - Collaborate with engineers to deploy and integrate successful model experiment results into large-scale, complex Amazon production systems with low latency. - Provide machine learning thought leadership to both technical and business leaders, with the ability to think strategically about business, product, and technical challenges. - Be a subject matter expert in reinforcement learning approaches for the team and actively contribute to the science roadmap - Define the science roadmap and research agenda that aligns with the organisation's priorities and production constraints. - Work with technical product managers to work backwards from what's important to customers and deliver machine-backed solutions. - Report and share results with the team and wider scientific community by authoring documents that are both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment. A day in the life You will be both a research leader and a hands-on innovator within the Commerce Insights organisation. You'll collaborate with talented engineers and senior leaders to solve problems that are uniquely challenging at Amazon's scale: personalising commerce decisions across multiple business lines balancing competing objectives across offerings, and positively impacting hundreds of millions of customers worldwide. The problems here are technically deep — combining large-scale ML, causal reasoning, and behavioural modelling in a domain where every decision carries real revenue and customer experience consequences. Your research will ship to production and move metrics that matter. About the team You will join a team of great team of engineers and applied scientists with a proven track record of solving highly complex, ambiguous problems — work that has produced patents and publications at top-tier conferences. The team has direct visibility to senior Prime Video leadership, and collaborates broadly across Commerce, Content, and Platform teams to shape how customers discover, subscribe to, and engage with video content. This is a team that operates at the intersection of rigorous research and real-world impact, where your ideas move from whiteboard to production for hundreds of millions of customers.

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. Amazon Music Search Science team is seeking an experienced Applied Scientist who will join a team of experts in the field of machine learning, and work together to break new ground in the world of understanding and classifying different forms of music, and creating interactive experiences to help users find the music they are in the mood for. We work on machine learning problems for music classification, recommender systems, dialogue systems, NLP, and music information retrieval. You'll work in a collaborative environment where you can pursue applied research, with many peta-bytes of data, work on problems that haven’t been solved before, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers, and publish your research. You'll see the work you do directly improve the experience of Amazon Music customers on Alexa/Echo, mobile, and web. Key job responsibilities - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Work closely with software engineering teams to drive model implementations and new feature creations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Research and implement novel machine learning and statistical approaches About the team Everyone on our team has a meaningful impact on product features, new directions in music streaming, and customer engagement. We are looking for new team members across a variety of job functions including software engineering/development, marketing, design, ops and more. Come join us as we make history by launching exciting new projects in the coming year.Our team is focused on building a personalized, curated, and seamless music experience. We want to help our customers discover up-and-coming artists, while also having access to their favorite established musicians. We build systems that are distributed on a large scale, spanning our music apps, web player, and voice-forward audio engagement on mobile and Amazon Echo devices, powered by Alexa to support our customer base. Amazon Music offerings are available in countries around the world, and our applications support our mission of delivering music to customers in new and exciting ways that enhance their day-to-day lives.

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 add-on subscriptions such as Apple TV+, Max, 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 technologist, 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! Prime Video is disrupting traditional media with an ever-increasing selection of movies, TV shows, Emmy Award-winning original content, add-on subscriptions, and live events like Thursday Night Football. Within this expanding ecosystem, Linear TV with its 24/7 scheduled broadcast-style programming has emerged as one of our fastest-growing segments, with viewership hours increasing significantly year over year. This growth demonstrates that even in the streaming era, customers deeply value the lean-back, curated experience that Linear TV provides. Key job responsibilities As an Applied Scientist on LPEX, you will be a technical owner and science leader across the following areas: * Define and drive the science strategy and multi-year roadmap for Linear TV personalization, translating research advances into measurable business and customer experience outcomes. * Design, develop, and deploy machine learning models for content recommendation, viewer engagement optimization, and real-time personalization at the scale of hundreds of millions of Prime Video customers. * Own the complete ML lifecycle: problem formulation, data analysis, feature engineering, model development, offline and online evaluation, and reliable production deployment. * Build and continuously optimize recommendation systems with strict real-time latency requirements, ensuring that personalization decisions are delivered at speed and scale. * Design and execute rigorous A/B and multivariate experiments to measure recommendation quality, understand causal drivers of engagement, and iterate rapidly toward customer impact. * Partner with software engineering teams to productionize ML models, defining requirements for serving infrastructure, data pipelines, and model monitoring and observability. * Collaborate with product managers and cross-functional stakeholders to translate ambiguous business problems into well-scoped, tractable science solutions. * Publish research findings and contribute to the broader scientific community through papers, patents, and internal knowledge-sharing forums. * Mentor scientists and engineers on the team, setting a high bar for scientific rigor, experimental discipline, and ML engineering best practices. A day in the life We are looking for an Applied Scientist who will define and drive the science strategy for personalization and recommendations on Linear TV. You will own the end-to-end machine learning lifecycle from problem formulation and research through experimentation and production deployment, building systems that help millions of customers discover the right content at the right time. It's Day 1 for personalizing the linear TV experience on Prime Video, and you will be at the forefront of this innovation. About the team The Linear Personalization Experience (LPEX) team is building next-generation, AI-powered personalization and recommendation systems to enhance this natural engagement and deliver a best-in-class Linear TV experience for Prime Video customers worldwide. The LPEX team's vision is to surface the breadth and depth of Prime Video's linear selection at exactly the right moment for each customer curating the most relevant programming, tailored to individual tastes, purchase behaviors, schedules, and viewing habits, while simultaneously elevating awareness of our extensive live and linear catalog. Our mission is to anticipate and exceed viewers' expectations, fostering deeper connections with the content they love. We adapt to viewers' preferences and propensities for both live and on-demand viewing, enriching the overall entertainment journey. The team operates at the intersection of machine learning research, large-scale distributed systems, and consumer product strategy, partnering closely with product management, engineering, and business development.