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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.

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

Employer: Amazon Web Services, Inc. Position: Applied Scientist II - AMZ27496.1 Location: Boston, MA Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. (40 hours / week, 8:00am-5:00pm, Salary Range $161803 - $193200) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000

As part of the AWS Applied AI Solutions organization, we have a mission to build delightful AI solutions that improve human capabilities and business outcomes. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated AI solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. The Applied Scientist will contribute to the development of Agentic AI solutions leveraging Gen AI. The role requires extending existing scientific techniques and inventing new ones to address specific customer needs. You should be comfortable working semi-autonomously on difficult problems with visible risks or roadblocks. You'll work closely with technical leaders within the team. We're looking for scientists who can maintain high standards while moving quickly, prioritizing both rapid experimentation and responsible AI development to deliver measurable customer impact. Key job responsibilities * Design and implement solutions that extend or adapt scientific approaches for customer needs * Deliver components into production that meet high quality standards (efficient, reproducible, testable code) * Produce science outputs demonstrating correctness, scholarship, and scientific rigor * Work with product, engineering, and science teams to deliver impactful AI features * Contribute to operational excellence in the team's deliverables About the team AWS Solutions As part of the AWS solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use.

Join the AWS Perimeter Protection team as a Senior Applied Scientist, where you will bring your deep ML engineering expertise to design, build, and scale AI-driven security solutions that protect AWS customers worldwide. This role is ideal for someone who has already built and shipped production ML systems at industry scale and is looking to apply that experience to high-impact security challenges. You will own the full ML lifecycle — from research and prototyping to production deployment and optimization — powering services including Web Application Firewall, DDoS Protection, Bot Management, and Infrastructure Protection. With services spanning all AWS regions and handling trillions of requests per week, you will solve complex engineering and science problems where model performance, system reliability, and low-latency inference are critical. Key job responsibilities - Design, build, and deploy production-grade ML models and systems for real-time threat detection, mitigation, and protection against evolving cyber threats at cloud scale. - Own the full ML lifecycle end-to-end — from problem formulation, data engineering, and model development through to production deployment, monitoring, and continuous improvement. - Architect and optimize ML pipelines, training infrastructure, and serving systems to meet strict latency, throughput, and reliability requirements at AWS scale. - Bridge the gap between research and production by translating novel ML approaches into robust, scalable, and maintainable systems that operate in real-time security environments. - Design and implement feature engineering workflows and large-scale data processing pipelines to support rapid experimentation and reliable model iteration. - Collaborate closely with software engineering teams to integrate ML models into distributed, low-latency security services, driving engineering decisions around model serving, infrastructure, and system design. - Analyze large-scale production data to identify patterns, anomalies, and emerging threat vectors, and translate findings into measurable improvements to detection and mitigation capabilities. - Establish and improve best practices for ML system design, model evaluation, A/B testing, and production monitoring across the team. - Mentor junior scientists and engineers, raising the bar on both scientific rigor and engineering quality.

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.

We are searching for a talented candidate with expertise in orbital mechanics and spaceflight navigation, including LEO Satellite Orbit Determination. This position requires experience in simulation and analysis of spacecraft orbital mechanics and sequential orbit determination methods, including Extended Kalman Filters (EKF) and/or Unscented Kalman Filter (UKF). Strong analysis skills are required to develop engineering studies of complex large-scale dynamical systems. This position requires demonstrated expertise in computational analysis automation and tool development. Key job responsibilities - Perform spacecraft maneuver or navigation analysis in support of multi-disciplinary trades within the Amazon Leo team. - Contribute to prototype software development of flight algorithms. - Test and assess navigation software for integration into flight systems. - Assess and trouble-shoot the performance of Leo on-board GNSS hardware and software systems. - Work closely with GNC engineers to manage on-orbit performance and develop flight dynamics operations processes. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. A day in the life - Interacting with GNC teams to evaluate and troubleshoot satellite issues. - Working within the Flight Dynamics Research team to prioritize tasks. - Performing analysis, simulation, testing and documentation to address assigned tasks.

Are you passionate about helping customers achieve business transformation through AI? Do you want to lead forward-deployed teams that embed directly into the enterprise and unlock real business outcomes? And are you ready to operate as a general manager across engineering, science, and commercial strategy in the fastest-moving space in AI and infrastructure? The AWS Generative AI Innovation Center (GenAIIC) is on a mission to accelerate enterprise AI transformation across global customers going from beyond isolated use cases to holistic, C-suite-sponsored initiatives that reshape how organizations operate. We combine deep AI expertise across science, strategy, and business transformation. We start with the customer's most critical operational challenges and work backwards, and deploy multidisciplinary teams that embed with the customer, prove impact in 45-day sprints, and expand across the enterprise. We are a fast-moving, entrepreneurial team that values leaders who can operate across technical depth and commercial breadth. You will lead a team of ML engineers, AI scientists, and AI strategists who work alongside customers to architect and deliver AI solutions that move and stay in production, realizing value. You will regularly engage with CFOs, CIOs, and C-suite executives. You must bring equal fluency in engineering, data science, go-to-market, and customer delivery. You are ready to roll up your sleeves alongside the team, whether that means scoping an agentic AI architecture, presenting to a board, or operationalizing a repeatable delivery motion. You will partner with customers, AWS Sales, AWS service teams, AWS industry teams and AWS Professional Services delivery teams to meet the specific needs of the customer, and extend that use to other customers. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

The Amazon Search team creates customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. The Search Relevance team focuses on several technical areas for improving search quality. In this role, you will invent universally applicable signals and algorithms for training machine-learned ranking models. The relevance improvements you make will help millions of customers discover the products they want from a catalog containing millions of products. You will work on problems such as predicting the popularity of new products, developing new ranking features and algorithms that capture unique characteristics, and analyzing the differences in behavior of different categories of customers. The work will span the whole development pipeline, including data analysis, prototyping, A/B testing, and creating production-level components. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world’s leading Internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Please visit https://www.amazon.science for more information Key job responsibilities Your responsibilities include but not limited to: * Analyze the data and metrics resulting from traffic into Amazon's product search service. * Design, build, and deploy effective and innovative ML solutions to improve search ranking. * Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production. * Publish and present your work at internal and external scientific venues in the fields of ML/NLP/IR.

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