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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.

Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? Answer yes to any of these questions and you will be a great fit for our team at Amazon. As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for Personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Key job responsibilities - Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to optimize the shopping experience, and run these models directly on the retail website. - Develop AI solutions for Recommendation systems using Deep learning, LLMs, Reinforcement Learning, distillation, and Optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Publish your research findings in top conferences and journals. About the team Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. We run global experiments and our work has revolutionized e-commerce with features such as "Keep shopping for ...", “Customers who bought this item also bought”, and “Frequently bought together”.

Amazon is the 4th most popular site in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. The Search Query Understanding team is at the forefront of revolutionizing the online shopping experience through the Amazon search page. Our ambition is to transform the search engine into a shopping engine. Leveraging advances in Large Language Models (LLMs), we aim to deeply understanding our users' shopping missions, preferences, and goals. By developing responsive and scalable solutions, we not only accomplish the shopping mission but also foster unparalleled trust among our customers. Through our advanced technology, we generate valuable insights, ensuring a comprehensive and holistic shopping experience. Our dedication to continuous improvement through constant measurement and enhancement of the shopper experience is crucial, as we strategically navigate the balance between immediate results and long-term business growth. We are seeking an Applied Scientist who is not just adept in the theoretical aspects of Machine Learning (ML), Artificial Intelligence (AI), and Large Language Models (LLMs) but also possesses a pragmatic, hands-on approach to navigating the complexities of innovation. The ideal candidate should have a profound expertise in one or more areas including but not limited to Retrieval Augmented Generation (RAG), post-training of foundation models and LLM inference optimizations. You will take the lead in conceptualizing, building, and launching innovative models that significantly improve our understanding of search missions and capabilities in enhancing the search experience. A successful applicant will display a comprehensive skill set across machine learning model development, implementation, and optimization. This includes a strong foundation in data management, software engineering best practices, and awareness of the latest developments in model lifecycle management. We are looking for individuals who are analytically rigorous, passionate about applied sciences, creative, and possess strong logical reasoning abilities. Join the Search Query Understanding team, a group of pioneering ML scientists and engineers dedicated to building core ML models and developing the infrastructure for model innovation. As part of Amazon Search, you will experience the dynamic, innovative culture of a startup, backed by the extensive resources of Amazon.com (AMZN), a global leader in internet services. Our collaborative, customer-centric work environment spans across our offices in Palo Alto, CA, and Seattle, WA, offering a unique blend of opportunities for professional growth and innovation. Key job responsibilities Collaborate with cross-functional teams to identify requirements for ML model development, focusing on enhancing mission understanding through innovative AI techniques, including retrieval-Augmented Generation or LLM in general. Design and implement scalable ML models capable of processing and analyzing large datasets to improve search and shopping experiences. Must have a strong background in machine learning, AI, or computational sciences. Lead the management and experiments of ML models at scale, applying advanced ML techniques to optimize science solution. Serve as a technical lead and liaison for ML projects, facilitating collaboration across teams and addressing technical challenges. Requires strong leadership and communication skills, with a PhD in Computer Science, Machine Learning, or a related field.

We are seeking a scientist with deep programmatic and Ads experience to further the development and application of analytics methods to examine the complex data flows of the entre Amazon Ads system, and to translate deep-dives into actionable insights for our product teams. In this role you will develop new tools to analyze our advertising data to help improve the performance of our bidding algorithms, targeting and relevance systems, help advance our 3rd party and O&O supply strategy, and evaluate the adoption and impact of feature releases. Key job responsibilities • Analyze data trends regarding supply, optimization, ad load, and advertising mix effects that affect advertiser performance and contribute to achieving advertiser goals. • Present papers to senior leaders on issues like feature development impact on identity recognition rates, and changes of ad selection systems to improve fill rate highlighting insights that will inform our business development and engineering roadmaps. • Formalize our analytics approach to the DSP auctions by analyzing bid spreads, auction depth, and simulating impacts of potential auction structure changes. • Identify, standardize, and operationalize KPIs to effectively measure the performance of all systems involved in ad serving, and use trend insights to inform business priorities. • Partner with engineering teams to define data logging requirements and getting these prioritized in engineering roadmaps. • Validate financial models through analysis. • Develop and own ad revenue and supply intelligence analytics decks that provide ongoing deep-dives. A day in the life The DSP Analytics Leader will work closely with business leaders and engineers on developing common data architecture that will optimize our data logging at different grains, and will allow data interoperability from bid flow to optimization to campaign delivery. The candidate will then analyze the data and present papers and ongoing reports on actionable insights. About the team The DSP Ads Product Analytics Team's Mission: Work alongside those who need product data to apply objective perspective and business logic to uncover insights, advise strategic decisions, and adjust to industry changes.

We are seeking an Applied Scientist to join Amazon Robotics, Compass Team. In this role, you will own the development of safe legged locomotion algorithms and their deployment on physical hardware, developing learning-based controllers that enable quadrupeds and humanoids to walk, run, and recover from disturbances with agility and robustness. You will leverage Reinforcement Learning (RL), sim-to-real transfer, and other learning-based architectures to train policies that produce stable, dynamic gaits across varied terrains and operating conditions. These learned policies will interface with model-based control strategies to form whole-body control laws that balance performance and safety. Your work sits at the novel intersection of safety and machine learning, where these learned policies will be used in a safety-critical context for complex safety constraints like stability. You will collaborate closely with perception, planning, and safety teams to close the loop between what the robot sees, where it needs to go, and how it moves to get there safely. This is a rare opportunity to shape how legged robots move through the world alongside people. Key job responsibilities • Design, train, and deploy reinforcement learning policies for dynamic legged locomotion including walking, running, stair climbing, and fall recovery on physical quadruped and humanoid platforms • Collaborate with the Compass safety team to ensure locomotion policies operate within safety-critical bounds, incorporating control barrier functions or other formal safety mechanisms as constraints during or after training • Develop sim-to-real transfer pipelines that produce policies robust to the reality gap, including domain randomization, system identification, and adaptive strategies • Integrate learned locomotion policies with model-based whole-body controllers, defining how RL outputs (e.g., joint targets, contact schedules) interface with optimization-based control layers • Formulate reward functions and training curricula that encode both performance objectives and safety constraints, ensuring policies respect stability and contact-force limits • Develop and maintain large-scale training infrastructure for locomotion policy learning, including physics simulation environments and parallelized training pipelines • Evaluate policy performance rigorously through simulation benchmarks, hardware experiments, and failure-mode analysis • Investigate emerging techniques (e.g., foundation models for control, diffusion policies, world models) and assess their applicability to safe legged locomotion • Publish research at top-tier robotics and ML venues and contribute to Amazon's scientific reputation in legged 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 inventors of control barrier functions on a novel, universal approach to safe autonomy: one that scales across mobile robots, manipulators, mobile manipulators, and future robot platforms with dynamic stability. You'll push the boundary of safe performance by integrating safety with motion planning, RL, and foundation models, ensuring that safety is never a blocker to robot performance. Your work will underpin robots operating alongside people at Amazon's unprecendented scale.

We are seeking an Applied Scientist to join Compass. In this role, you will own the interface between contact-rich manipulation and the Compass safety software in unstructured environments. You will design learning-based and model-based approaches to contact-rich manipulation when the environment changes unexpectedly. You will collaborate with perception, planning, and controls teams to close the loop from object detection through grasp execution, and you will deploy your algorithms on physical hardware across multiple manipulator platforms. This is an opportunity to define how Amazon's robots safely interact with the physical world: picking, placing, and handling the enormous diversity of objects that flow through our network. Key job responsibilities • Develop and deploy manipulation algorithms for contact-rich tasks and placement across diverse object geometries and material properties • Design force-controlled manipulation strategies that operate safely within Amazon Compass safety constraints • Build reactive manipulation policies that detect and recover from failures (slips, missed grasps, unexpected contacts) in real time • Develop learning-based manipulation policies using RL, imitation learning, or hybrid approaches, and transfer them from simulation to physical hardware • Define and maintain the interface contract between manipulation algorithms and the Compass safety layer, ensuring that grasp and motion plans respect safety bounds without unnecessary conservatism • Collaborate with perception teams to leverage object pose estimation, tactile sensing, and contact detection for closed-loop manipulation • Design simulation environments and training curricula for manipulation policy learning, including realistic contact physics and object diversity • Evaluate manipulation performance through systematic hardware experiments, measuring grasp success rates, cycle times, and safety compliance • Contribute to scientific publications and internal technical documentation • Participate in cross-team design reviews and contribute to the broader manipulation and safety architecture 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 inventors of control barrier functions on a novel, universal approach to safe autonomy: one that scales across mobile robots, manipulators, mobile manipulators, and future robot platforms with dynamic stability. You'll push the boundary of safe performance by integrating safety with motion planning, RL, and foundation models, ensuring that safety is never a blocker to robot performance. Your work will underpin robots operating alongside people at Amazon's unprecendented scale.

We are seeking a Principle Applied Scientist to join Compass. In this role, you will own the perception input into the Compass safety system, defining how robots perceive, interpret, and anticipate their surroundings in safety-critical contexts. You will develop novel approaches to environment understanding that go beyond static scene representation, providing real-time, predictive models of how humans, objects, and dynamic obstacles may evolve over short time horizons. Your work will directly unlock robot performance by replacing conservative assumptions with precise, learned understandings of risk. You will set the scientific direction for perception within Compass, collaborate closely with controls, planning, and firmware teams, and influence the broader Amazon Robotics safety architecture. Key job responsibilities • Define and drive the long-term scientific vision for safety-critical perception within Compass, spanning multiple robot platforms and deployment environments • Develop novel perception algorithms that provide real-time, predictive representations of dynamic environments including human motion forecasting, obstacle trajectory prediction, and scene evolution modeling • Design perception outputs that are tightly coupled to safety constraints, enabling control barrier functions to operate with minimal conservatism while maintaining formal safety guarantees • Research and develop methods to quantify and bound perception uncertainty, providing calibrated confidence estimates that safety systems can reason over • Architect perception pipelines that generalize across sensor modalities (LiDAR, depth cameras, RGB, radar) and robot morphologies without platform-specific retraining • Investigate the application of foundation models and large-scale pre-training to safety-critical perception tasks, establishing when and how learned representations can be trusted at safety-critical confidence levels • Collaborate with controls, motion planning, and firmware teams to define interface contracts between perception and downstream safety modules • Publish research at top-tier venues and represent Amazon Robotics in the broader academic and industry community • Mentor and develop a team of applied scientists and research engineers • Influence Amazon Robotics' safety architecture and perception strategy at the organizational level About the team Work with the inventors of control barrier functions on a novel, universal approach to safe autonomy. You'll push the boundary of safe performance by integrating safety with motion planning, RL, and foundation models, ensuring that safety is never a blocker to robot performance.

The Japan Prime & Marketing team drives customer growth and engagement for Amazon Japan. Our applied science team combine advanced machine learning with deep business understanding to deliver experiences that delight customers and grow the Prime membership base in one of Amazon's most dynamic and competitive marketplaces. We are seeking a Senior Applied Scientist to lead the science for personalization and customer growth initiatives across Japan Points, promotional campaigns, and Prime membership engagement. You will own end-to-end science solutions — from problem formulation and data analysis through model development, A/B testing, and production deployment — that directly impact millions of Japanese customers. This is a high-visibility role where you will define the science roadmap, influence business strategy with data-driven insights, and collaborate with product, engineering, economics, and marketing teams across Japan and globally. Key job responsibilities - Define and execute the science roadmap for personalization, points optimization, promotions targeting, and customer growth within Japan Prime & Marketing - Design and develop machine learning models for customer segmentation, lifetime value prediction, churn propensity, and next-best-action recommendation to drive Prime acquisition and retention - Build optimization frameworks for Japan Points allocation, promotional offer targeting, and budget efficiency that maximize long-term customer value rather than short-term engagement - Apply causal inference, experimentation design, and econometric methods to measure the incremental impact of points, promotions, and marketing interventions - Develop personalization systems that tailor offers, messaging, and incentive structures to individual customer preferences and lifecycle stages - Lead the design and analysis of large-scale A/B tests and quasi-experimental studies to validate model performance and business impact - Collaborate with engineering teams to integrate models into production systems with millisecond-level latency requirements serving millions of daily active customers - Influence senior leadership through clear communication of scientific findings, trade-offs, and strategic recommendations - Mentor junior scientists and raise the scientific bar across the team through code reviews, design reviews, and knowledge sharing - Contribute to the broader scientific community through internal and external publications at peer-reviewed venues

We are seeking a Member of Technical Staff Simulation Engineer to join our AI robotics research team developing foundation models for robotics. You will rapidly develop 3D physics-based and photorealistic simulations alongside scientists to enable training large-scale machine learning models. Key job responsibilities - Develop simulations for reinforcement learning, closed-loop simulations and synthetic data generation - Implement essential robotics features, including accurate modeling of sensors, actuators, and controllers - Build real-to-sim workflows for dynamic environments and robotics tasks - Implement simulation features to minimize sim-to-real gaps through domain randomization and system identification - Create asset toolchains supporting industry-standard formats (URDF, MJCF, USD) - Collaborate closely with a team of ML researchers to enable large-scale robotics training pipelines About the team At Frontier AI & Robotics (FAR), we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.

Alexa AI is looking for an Applied Scientist to build Alexa+, Amazon's LLM-powered conversational assistant. You will work on key initiatives spanning large language model fine-tuning, alignment, agentic reasoning, and evaluation — directly shaping the experience for hundreds of millions of customers worldwide. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with other scientists and engineers to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Key job responsibilities * Improve the efficiency of LLM, VLM, and agent training and evaluation pipelines, including distributed training, inference serving, data loading, checkpointing, memory usage, and GPU utilization. * Design, implement, and evaluate novel approaches to LLM fine-tuning, alignment (RLHF, DPO), and distillation for production deployment * Architect agentic systems — multi-step reasoning, tool use, planning, and orchestration * Develop evaluation frameworks and methodologies that go beyond standard benchmarks to capture real-world conversational quality * Translate research advances into customer-facing products, working closely with engineering, product, and cross-functional science teams * Publish results at top-tier venues and represent Amazon in the broader research community About the team Alexa AI is building the science and technology behind Alexa+, Amazon's next-generation conversational assistant. Our team works at the intersection of large language models, reinforcement learning, agentic architectures, and multilingual/multimodal understanding. We operate at massive scale — our models serve customers across dozens of languages and device types. If you want to push the frontier of conversational AI and see your work used by people every day, come join us.