Careers

Lead a team of scientists who measure what matters—the real-world impact of Amazon’s marketing at billion-dollar scale. Amazon Advertising is one of Amazon’s fastest growing lines of business and our team occupies a unique position focused on maximizing the success of Amazon’s own marketing programs using our ad tech. Amazon marketing is Customer Zero for our ad tech, and our measurement science team is focused on quantifying the impact of the marketing that drives customers to discover and engage with Amazon’s products and services. We turn petabytes of retail, streaming, and customer signals into causal insights that make Amazon’s marketing spend smarter and more effective. As Customer Zero, we also extend the impact of methodologies, measurement systems, and insights we generate for Amazon’s marketing to directly inform the products and services we offer to external advertisers, shaping the tools that power the broader Amazon Ads ecosystem. This is a growth role for a successful candidate that is energized by building high-performing teams, tackling causal inference challenges that don’t have textbook answers, and shipping science that improves both Amazon’s own marketing and the advertising products used by the world’s largest brands. Dozens of Amazon businesses use Amazon’s ad tech for their marketing objectives, driving more than $1B of marketing investments through Ads services and tools. As part of our team, this role will lead a cross-functional science team to measure the impact of Amazon’s marketing and identify opportunities for optimization at scale. You will set the strategic direction for your team, drive initiatives to make smarter marketing decisions, and improve the relevance of advertising to our customers. We move away from industry standard measurement systems and build sophisticated and insightful decision engines. We enable massive advertising programs, generating billions of impressions decorated with rich representations of customer state. The major challenges we are solving include integrating petabyte-scale distributed retail systems into a singular service to synthesize e-commerce data into measurement and optimization models. The successful candidate will have a strong technical background in ML and causal inference, experience with large scale data ML models, high judgment to drive business impact, an appreciation for white-space, and success building and developing high-performing teams. Key job responsibilities • Lead, hire, mentor, and develop a team of scientists and engineers, fostering a culture of innovation, scientific rigor, and operational excellence. • Set the technical vision and roadmap for the team’s measurement and modeling initiatives, aligning with business priorities. • Partner with senior leadership to define measurement strategies and translate business needs into science roadmaps and deliverables. • Guide the team in applying ML, statistics, and econometrics to develop, analyze, and productionize prototype models. • Drive the design and analysis of large-scale online experiments to validate models and measure impact. • Foster collaboration across science teams through peer-review processes, publishing research internally and at industry conferences. • Establish scalable, efficient, and automated processes for large-scale model development, validation, and implementation.

AWS Infrastructure Services Science (AISS) researches and builds machine learning models that influence the power utilization at our data centers to ensure the health of our thermal and electrical infrastructure at high infrastructure utilization. As an Applied Scientist, you will work on our Science team and partner closely with other scientists, data engineers and Software teams to accurately model and optimize our power infrastructure. Outputs from your models will directly influence our data center topology and will drive exceptional cost savings. You will be responsible for researching and deploying machine learning models that optimize our power and thermal infrastructure, working across AWS to solve data mapping and quality issues and contribute to our Science team vision. You are skeptical. When someone gives you a data source, you pepper them with questions about sampling biases, accuracy, and coverage. When you’re told a model can make assumptions, you proactively try to break those assumptions. You have passion for excellence. The wrong choice of data could cost the business dearly. You maintain rigorous standards and take ownership of the outcome of your data pipelines and code. You raise the bar on the software code standards to delivery faster and efficient solutions. You do whatever it takes to add value. You don’t care whether you’re building complex ML models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and preserve customer availability. You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team. You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to learn Data Center architecture and components of electrical engineering to build your models. You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer availability are constantly up for debate among senior leadership - you will help drive this conversation. This position requires superior analytical thinkers, able to quickly approach large ambiguous problems and apply their technical and statistical knowledge to identify opportunities for further research. You should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. Successful candidates must thrive in fast-paced environments which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon’s strategic needs. Key job responsibilities - Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems - Disambiguate problems to propose clear evaluation frameworks and success criteria - Work autonomously and write high quality technical documents - Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production - Work closely with other scientists to deliver impactful customer solutions - Share and publish works with the broader scientific community through meetings and conferences - Communicate clearly to scientific audiences

Amazon's Customer Experience and Business Trends (CXBT) is looking for an Applied Scientist to help shape the science of evaluating agent-driven builder experiences on AWS. As builders increasingly delegate goals to autonomous coding agents, the agent's experience becomes the builder's experience — and measuring it rigorously is an open research problem. At Amazon Benchmarking, we are developing new scientific methodologies to evaluate how autonomous agents perceive, reason about, and build on AWS. This role sits at the intersection of GenAI, agent evaluation, and developer-experience research. You will design principled measurement methodologies that treat autonomous agents as instruments, capturing behavioral evidence rather than self-reported outcomes and develop rigorous, reproducible techniques for reasoning about why agents make the architectural decisions they do. Evaluating those decisions demands a scientist who understands cloud architecture and services in depth: to reason about the choices an agent makes, you must understand the trade-offs it is navigating. Our team's mission is to constantly challenge the status quo and drive innovation to improve customer experience. This role requires an expert in the areas of GenAI and machine learning with a keen interest in research. The ideal candidate will have experience with deep learning models for natural language processing tasks, agentic/tool-using systems, and the evaluation of autonomous agents, along with a deep working knowledge of cloud architecture and services. A successful candidate will have a passion for clarity, be a self-starter comfortable with ambiguity, have strong attention to detail, be able to work in a fast-paced and entrepreneurial environment and be driven by a desire to innovate. Given the complex nature of the questions we are working on, the ideal candidate will be relentlessly curious and be able to/be interested in having the business conversation underlying the data. Key job responsibilities - Develop novel scientific methodologies for evaluating autonomous, tool-using agents, advancing the state of the art in agent behavioral measurement and developer-experience research. - Design rigorous, reproducible experiments that yield causal insight into agent decision-making, controlling for confounds and ensuring findings are grounded in observed behavior rather than self-report. - Apply deep knowledge of cloud architecture and services to reason about the architectural decisions agents make — the service trade-offs, integration constraints, and deployment considerations that shape what gets built. - Develop verification and synthesis methods that ensure only evidence-grounded conclusions survive, advancing techniques for reliability and faithfulness in agent evaluation. - Design evaluation methodologies and metrics that quantify the quality of agent-produced outcomes across dimensions such as correctness, robustness, and security posture. - Publish and present your work at internal and external scientific venues in the fields of ML. - Collaborate with scientists, engineers, and product managers to design and implement science-focused systems. - Proficiency in model development, validation and implementation for NLP applications. - Excellent verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts. A day in the life As a scientist on the team, you will be involved in every aspect of the process from idea generation, business analysis and scientific research, through development and deployment of advanced models and agentic systems, giving you a real sense of ownership. You'll research, design, and build new methodologies to evaluate the experience of autonomous agents building on AWS — developing the measurement science that lets us reason rigorously about what agents do and why. Besides theoretical analysis and innovation, you will work closely with talented engineers and put your algorithms and models into practice. About the team Customer Experience and Business Trends (CXBT) is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers). Our approach is based on determining the customer need, along with problem solving, and we work backwards from there. We use technical and non-technical approaches and stay aware of industry and business trends. We are a global team, made up of a diverse set of profiles, skills, and backgrounds – including: Scientists, Product Managers, Software Developers, Computer Vision experts, Solutions Architects, Business Intelligence Engineers, Business Analysts, Risk Managers, and more.

Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative 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.

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! Prime Video is pioneering the use of Generative AI to empower the next generation of creatives. Our mission is to make world-class media creation accessible, scalable and efficient. We are seeking an Applied Scientist to advance the state of the art in Generative AI and to deliver these innovations as production-ready systems at Amazon scale. Your work will give creators unprecedented freedom and control while driving new efficiencies. Key job responsibilities As an Applied Scientist, you will have end-to-end ownership of the product, related research and experimentation. In addition, you will be applying advanced machine learning techniques in Computer Vision, Multimedia Understanding and Generative AI. We're building the foundational technology stack, spanning diffusion and flow-matching models, 3D/4D scene and character generation, motion and camera control, and post-training alignment. Other responsibilities include: - Research and develop generative models for controllable synthesis across images, video, vector graphics, and multimedia - Innovate in advanced diffusion and flow-based methods (e.g., inverse flow matching, parameter efficient training, guided sampling, test-time adaptation) to improve efficiency, controllability, and scalability - Advance visual grounding, depth and 3D estimation, segmentation, and matting for integration into pre-visualization, compositing, VFX, and post-production pipelines - Design multimodal GenAI workflows including visual-language model tooling, structured prompt orchestration, agentic pipelines

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.

Lead ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Sr Applied Scientist, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.

As a member of a growing PNT team within Amazon Leo, the PNT Estimation and Algorithms Engineer is responsible for designing, implementing, optimizing and monitoring high-precision estimation architectures across terrestrial and space-based networks. You will develop and validate orbit determination, filtering, and sensor fusion algorithms to achieve high precision positioning and timing accuracy using RF and optical inter-satellite link measurements. 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. Key job responsibilities Design, implement, and validate orbit determination (OD) and clock estimation algorithms, including Extended Kalman Filters (EKF), Batch Least Squares, Square-Root/Sigma-Point Kalman Filters (SKF), and Factor Graph Optimization (FGO) approaches Develop inter-satellite link (ISL) navigation and time-transfer measurement models, including range and range-rate Evaluate and integrate emerging estimation techniques (batch/sequential estimation, robust filtering, multi-sensor fusion) to improve positioning and timing accuracy Design and build simulation and analysis frameworks (e.g., Monte Carlo CONOPS studies) to characterize filter performance, covariance realism, and convergence behavior Collaborate with cross-functional teams to ensure estimator outputs meet mission-level accuracy requirements Conduct performance analysis and troubleshooting of estimation algorithms across distributed constellation and ground infrastructure

Within Amazon's North American Operations, we are tackling mission-critical problems to optimize network operations. We build complex causal, forecasting, and optimization models to better understand, predict, and govern processes across hundreds of warehouses. We are seeking a Data Scientist to drive the development of these models, deeply integrate with business stakeholders, and leverage generative AI to more intelligently build for our customers. Key job responsibilities - Interfacing with stakeholders to understand business problems and translating these into high and low-level design documents. - Building machine learning, optimization, and causal inference models that target business-specific challenges. - Sourcing, cleaning, and analyzing large and complex data sets associated with warehouse operations to provide business-critical insights at scale. - Leveraging AI-based solutions to drive development and provide nuanced insights into model outcomes. A day in the life You will be a single-threaded owner of projects within Amazon Operations, owning project scoping, stakeholder engagement, data enablement, and model development. Your work will be directly consumed by partner teams throughout Operations and Finance, and hence a strong sense of ownership is required to educate both technical and non-technical stakeholders on developed science solutions. About the team Our team is comprised of Data Scientists, Applied Scientists, and Economists, and are trusted to make data-driven solutions that power operations decision making. We strive to build consensus on divisive topics through clear presentation of problem statements paired with deep analyses that gain visibility throughout all levels of the organization from operators to executives.

As an Applied Scientist, you will collaborate closely with other scientists and engineers to bring optimization and sequential decision-making research to production. This role combines the scientific application of ML, and specifically optimization, RL, and sequential decision making, with software development engineering and a strong product focus. It will be your job to design, implement, and deploy novel decision policies and optimization models in both prototype and production environments, and to prove their impact through rigorous evaluation and simulation before scaling them across the fleet. Key job responsibilities • Own the research and development of optimization and sequential decision-making solutions spanning constraint programming, stochastic and robust optimization, contextual bandits, and reinforcement learning for real-time MHE control and scheduling optimization in a production environment. • Formulate fulfillment operations and manufacturing scheduling problems (production scheduling, resource allocation, sorter optimization, throughput and congestion control) as optimization or sequential decision-making problems, and design multi-objective functions that balance competing operational objectives such as on-time delivery, utilization, changeover cost, and schedule stability. • Build and leverage high-fidelity simulation and emulation environments for safe offline training, policy validation, and transfer to live systems before fleet-scale deployment. • Collaborate across multiple science and engineering teams to integrate policies into production planning and real-time control systems, including monitoring, guardrails, and staged rollout. • Communicate results and their limitations clearly in writing to technical and business audiences, and contribute to the team's external research presence through publication where the work merits it. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, Computer Vision (CV), and Physics-Informed Neural Networks (PINNs). The team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.