Careers

Are you passionate about solving complex logistics challenges? Our Analytics team is at the forefront of enhancing delivery experiences through data-driven solutions and innovative technology. As a Research Scientist, you will join a team dedicated to optimizing our delivery network, ensuring reliable and efficient service to our customers. We are seeking an enthusiastic, customer-centric professional with strong analytical capabilities to drive impactful projects, implement advanced solutions, and develop scalable processes. In this role, you will have immediate ownership of business-critical challenges and the opportunity to make strategic, data-driven decisions that shape the future of our delivery operations. Your work will directly influence customer experience and operational excellence. The ideal candidate will possess both research science capabilities and program management skills, thriving in an environment that requires independent decision-making and comfort with ambiguity. This role offers the opportunity to make a significant impact on our advanced logistics network while working with pioneering technology and data science applications. Basic qualifications • 3+ years of building machine learning models for business application experience • Knowledge of programming languages such as C/C++, Python, Java or Perl • Experience programming in Java, C++, Python or related language • Experience with neural deep learning methods and machine learning Preferred qualifications: • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field • 5+ years of extensive relevant research experience • Deep expertise in Machine Learning • Proficiency in programming • Core competency in mathematics and statistics • Track record of successful projects in algorithm design and product development • Publications at peer-reviewed conferences or journals • Strategic thinker with good execution skills • Exhibits excellent business judgment • Effective verbal and written communication skills • Experience working with real-world data sets and building scalable models from big data • Experience with modern modeling tools and frameworks such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow • Experience with large scale distributed systems

We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

Do you want to lead the Ads industry and redefine how we measure the effectiveness of Amazon Ads business? Are you passionate about causal inference, Deep Learning & AI, raising the science bar, and connecting leading-edge science research to Amazon-scale implementation? If so, come join Amazon Ads to be a science leader within our Advertising Incrementality Measurement science team! Our work builds the foundations for providing customer-facing advertising measurement tools, furthering internal research & development, and building out Amazon's advertising measurement offerings. Incrementality is a lynchpin for the next generation of Amazon Advertising measurement solutions, and this role will play a key role in the release and expansion of these offerings. We are looking for a thought leader that has an aptitude for delivering customer-focused solutions and who enjoys working on the intersection of Big-Data analytics, Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to detail. You should be able to translate how data represents the customer journey, be comfortable dealing with large and complex data sets, and have experience using machine learning and/or econometric modeling to solve business problems. You should have strong analytical and communication skills, be able to work with product managers to define key business questions and work with the engineering team to bring our solutions into production. You will join a highly collaborative and diverse working environment that will empower you to shape the future of Amazon advertising, and also allow you to become part of our large science community. Key job responsibilities • Apply expertise in ML/DL, AI, and causal modeling to develop new models that describe how advertising impacts customers’ actions • Own the end-to-end development of novel scientific models that address the most pressing needs of our business stakeholders and help guide their future actions • Improve upon and simplify our existing solutions and frameworks • Review and audit modeling processes and results for other scientists, both junior and senior • Work with leadership to align our scientific developments with the business strategy • Identify new opportunities that are suggested by the data insights • Bring a department-wide perspective into decision making • Develop and document scientific research to be shared with the greater science community at Amazon About the team AIM is a cross disciplinary team of engineers, product managers, economists, data scientists, and applied scientists with a charter to build scientifically-rigorous causal inference methodologies at scale. Our job is to help customers cut through the noise of the modern advertising landscape and understand what actions, behaviors, and strategies actually have a real, measurable impact on key outcomes. The data we produce becomes the effective ground truth for advertisers and partners making decisions affecting millions in advertising spend.

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.

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 The science challenges include: - Heterogeneous customer responses. The same customer responds differently to ads depending on what they’re watching, how engaged they are, and their broader streaming context. We need to understand and predict this variation to make better advertising decisions in real time. - Signal sparsity in streaming. Unlike search or retail advertising, streaming offers no clicks, no conversions, and limited direct feedback. We must develop creative approaches to infer customer preferences, intent, and tolerance from indirect behavioral signals. - Personalization at scale. A one-size-fits-all ad experience leaves value on the table for both customers and the business. We build systems that adapt ad load, placement, and content to individual viewers across 100M+ customers and 100k+ titles. - Small effects, large variance. Ad interventions typically produce 0.1-2% shifts in engagement, effects easily overwhelmed by natural behavioral variance. Measuring, attributing, and optimizing these small signals requires rigorous experimental design and causal methodologies. - Competing objectives. Revenue, customer engagement, long-term retention, and advertiser value are in tension. We develop principled frameworks to navigate these tradeoffs and optimize for sustainable outcomes rather than any single metric. A day in the life - Lead the research and development of ML models that personalize advertising decisions for 100M+ customers across 100k+ titles, with production deployment in mind - Develop deep learning architectures (multi-task learning, embedding-based representations) for customer behavior prediction at scale - Design and analyze large-scale A/B experiments, applying causal inference techniques to measure and optimize the impact of ad strategies on customer engagement and monetization - Partner with engineering to ensure models meet production latency and scalability requirements - Collaborate with product managers to frame business problems as tractable ML problems and translate findings into product decisions - Shape the team’s scientific roadmap, identifying high-impact research directions About the team Prime Video Ads is pioneering the use of Generative AI, Deep Learning and Causal ML to empower the next generation of Ads experience in Prime Video. Our vision is to build the world’s most valuable and differentiated ad-supported streaming platform: one where every ad is relevant to the viewer, and every dollar spent by an advertiser delivers measurable results. We are seeking lead applied scientists who have demonstrated experience in advancing state of the art ML algorithms, particularly in Personalization, Relevance, Casual Inference and Gen-AI areas. Your role will be to deliver these innovations as production-ready systems at Amazon scale.

Are you inspired by invention? Do you want to see how your work impacts the bigger picture? If yes, you'll fit right in at Amazon's Last Mile Simulations and Analytics Engineering team. The WW AMZL Simulations and Analytics Engineering team drives innovation across our Last Mile network delivery stations through digital twin technology and the seamless integration of process engineering, physics, design, technology, and robotics. We start with the customer and work backwards in everything we do. If you're interested in joining a rapidly growing team building a unique, solutions-advisory group with a relentless focus on the customer, you've come to the right place. This is a blue-sky role where you'll roll up your sleeves and dive into big data sets to build digital twins—discrete event simulations, robotics models, and physics-based 3D simulations using tools like Flexsim, AnyLogic, and Emulate 3D. You'll develop experimentation systems at scale, integrating process workflows, physical dynamics, facility design, emerging technologies, and robotic automation to create comprehensive virtual replicas of our operations. Build optimization algorithms and leverage advanced technologies across Amazon. This is an opportunity to think big about solving challenging problems for our customers. As a Senior Simulation Scientist, you'll deep dive into complex problems and drive relentlessly towards innovative solutions with cross-functional teams. You'll interface and influence various functional teams and individuals at all levels of the organization. Lead strategic digital twin and simulation projects that integrate process design, physics modeling, technology innovation, and robotics to drive operational decisions. Your expertise in synthesizing and communicating insights to audiences of varying technical sophistication will enable you to answer specific business questions and innovate for the future. You'll apply advanced multi-disciplinary methodologies—combining process optimization, physics simulation, design thinking, and robotic systems—to complex use cases across the Last Mile network. You'll also contribute to the end-state vision for digital twin simulation and experimentation of future delivery stations at Amazon. Key job responsibilities • Lead the design, implementation, and delivery of the simulation data science solutions to perform system of systems discrete event simulations for significantly complex operational processes that have a long-term impact on a product, business, or function using FlexSim, Demo 3D, AnyLogic or any other Discrete Event Simulation (DES) software packages • Lead strategic modeling and simulation research projects to drive process design decisions • Be an exemplary practitioner in simulation science discipline to establish best practices and simplify problems to develop discrete event simulations faster with higher standards • Identify and tackle intrinsically hard process flow simulation problems (e.g., highly complex, ambiguous, undefined, with less existing structure, or having significant business risk or potential for significant impact • Deliver artifacts that set the standard in the organization for excellence, from process flow control algorithm design to validation to implementations to technical documents using simulations • Be a pragmatic problem solver by applying judgment and simulation experience to balance cross-organization trade-offs between competing interests and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors for multiple simulation projects • Provide simulation data and measurements that influence the business strategy of an organization. Write effective white papers and artifacts while documenting your approach, simulation outcomes, recommendations, and arguments • Lead and actively participate in reviews of simulation research science solutions. You bring clarity to complexity, probe assumptions, illuminate pitfalls, and foster shared understanding within simulation data science discipline • Pay a significant role in the career development of others, actively mentoring and educating the larger simulation data science community on trends, technologies, and best practices • Use advanced statistical /simulation tools and develop codes (python or another object oriented language) for data analysis , simulation, and developing modeling algorithms • Lead and coordinate simulation efforts between internal teams and outside vendors to develop optimal solutions for the network, including equipment specification, material flow control logic, process design, and site layout • Deliver results according to project schedules and quality A day in the life 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!

Employer: Amazon Data Services, Inc. Position: Manager III, Data Science - AMZ14164.12 Location: Herndon, VA Multiple Positions Available: 1. Lead a team of data scientists in developing and deploying machine learning models for infrastructure capacity planning and demand forecasting at global scale. 2. Design predictive models and statistical frameworks to optimize resource utilization and inform long-term infrastructure investment decisions. 3. Develop risk-based planning approaches utilizing advanced statistical methods and ML techniques to quantify uncertainty and set operational targets. 4. Build and maintain end-to-end ML pipelines for model training, inference, and monitoring in production environments. 5. Collaborate with engineering, product, and finance partners to translate complex business requirements into quantifiable metrics and automated decisionsupport systems. 6. Design experimentation frameworks and model validation methodologies to ensure accuracy and reliability of forecasts. 7. Lead weekly business reviews of model performance, presenting findings and recommendations to senior leadership. 8. Manage and mentor a team of data scientists and engineers across multiple levels, driving best practices in ML development, code quality, and production deployment. 9. Utilize Python, SQL, and cloud-based ML infrastructure to deliver scalable data science solutions. (40 hours / week, 8:00am-5:00pm, Salary Range $175100 - $236900) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000

Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Applied Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities - Drive the science vision for developing evaluations of the Ads Agent - Partner with senior and principal scientists from across Ads to develop best practices for evaluating agent performance both online and offline - Raise the science bar for all agent evaluations developed in Ads. - Deliver a data driven foundation for making decisions about when new features are ready to launch in Ads Agent.

The Automated Reasoning Group in the Amazon Neuron team is looking for an Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch and JAX. Your role will involve working closely with our custom-built Machine Learning accelerator, Trainium, which represents the forefront of innovation for advanced ML capabilities, and is the underpinning of Generative AI. In this role as an Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted AWS Neuron engineer, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, theorem provers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required.

Amazon Business System Integrations connects Amazon Business with millions of business users and thousands of partner procurement, ERP, and finance systems. We build the APIs, protocols, and agentic experiences that let customers buy on Amazon Business directly from the systems they already use. As an Applied Scientist on this team, you will own and advance the ML and GenAI capabilities that make these integrations smarter, faster, and more reliable. You will drive model accuracy, scientific innovation, and global scale across external systems integrations. Our work includes API-based and protocol-based shopping, such as punchout and emerging agent protocols, along with agentic interactions where buyers and AI agents search, compare, and purchase through natural language. It also covers partner onboarding and integration automation, mapping and normalizing catalog, product, and order data across many partner formats, and evaluation frameworks that measure the quality, accuracy, and latency of our agents. Key job responsibilities As an Applied Scientist on this team, you will work across three dimensions: Invent, Implement, and Influence. Invent: You bring deep domain knowledge and fluency with state-of-the-art scientific approaches and emerging research. You practice customer-obsessed science, working backwards from the needs of business buyers, procurement teams, and integration partners to extend or invent new ML approaches, even when no textbook solution exists. You design novel ML and LLM-based methods for agentic shopping, product search and matching, schema and data mapping across partner systems, and automated evaluation of agent quality. You help define the research agenda for our agentic and protocol-based capabilities, and you lead the scientific strategy for accurate, low-latency responses in conversational and API-driven procurement. You author or co-author articles for internal or external peer-reviewed venues when appropriate and not precluded by business considerations. Implement: The ML components you develop are integrated directly into production systems serving Amazon Business customers and partners worldwide. You make sound tradeoffs between accuracy and latency, innovation and stability, and short-term and long-term solutions, favoring reuse and established frameworks where appropriate. You make progress semi-autonomously with only occasional guidance, implement at the right level of complexity the first time, and evaluate emerging technologies, including LLMs and GenAI frameworks, largely on your own. You ensure your models and pipelines integrate robustly with catalog, offer, cart, order, and partner data sources and with the services that power external integrations. Influence: You contribute to planning for the team, including goals, priorities, and roadmaps for ML and GenAI capabilities. You lead the scientific strategy for expanding agentic and integration capabilities across new partners, protocols, and countries. You collaborate with engineering, product, and program teams across Amazon Business so that data integration and ML pipelines are robust and scale with adoption. You mentor scientists and engineers, champion best practices for the AI-Driven Development Life Cycle (AIDLC), and provide peer feedback on research methods and results. You help recruit bar-raising talent through interview drives, and you are visible in the broader scientific community as a subject matter expert, including serving as a Program Committee member or reviewer for peer-reviewed venues.