Careers

AI assistants are getting genuinely good at remembering individuals: your preferences, your projects, the thread you left open last week. But that memory stops at the edge of one person's usage. It doesn't reach the level at which real work happens, where the knowledge that matters is spread across many people, where one person's decision changes what everyone else should do next, and where nobody has the full picture. We're building AI that operates at that level: a durable, accurate understanding of how a team works, used to make that team measurably faster. We are looking for a Principal Applied Scientist to own the scientific direction of that work. This is a broad, ambiguous, high-leverage charter. The problems span knowledge representation, temporal reasoning, retrieval, agentic behavior, and the measurement science needed to know whether any of it is working. You will not be handed a well-posed problem. You will decide which problems are worth posing. This is a science leadership role, not a solo research role. You will set direction and raise the scientific bar across a team of applied scientists and MLEs, while staying deep enough in the work to prototype an idea yourself and prove it on real data. Key job responsibilities Own the scientific strategy for how organizational knowledge is represented, kept current, and retrieved: extraction, entity resolution, deduplication, graph structure, and retrieval that unifies graph, semantic, keyword, and temporal search. Advance temporal reasoning. Knowledge changes: facts are revised, decisions are reversed, priorities move. Representing what superseded what and when, and preserving the provenance to distinguish confirmed information from inferred information, is among the hardest open problems in this space. Define the science of proactive behavior. When is it right for an AI system to interrupt a human? These are precision-critical problems where a false positive costs far more than a miss, and where the right threshold varies by team and by individual. Lead our measurement science. Build evaluation for completeness and correctness across a multi-component agentic system, converging on a small number of trustworthy primary metrics rather than a sprawl of component scores. Judge honestly when an offline gain is real and when it is an artifact of a sparse dataset. Build the data that doesn't exist. The most valuable phenomena in this domain are also the rarest, which makes naturally occurring examples too scarce to learn from. Design synthetic and simulated data pipelines that generate controlled, realistic scenarios so these capabilities can be developed and tested at all. Own the learning loop. Turn human interaction into usable training signal, and set the direction for how the system improves from explicit feedback in the near term and from passive observation over the longer term. Make the efficiency calls. Decide where frontier models are required and where a smaller domain-tuned model is sufficient, and build the cost and capacity measurement that makes it a data-driven decision rather than an opinion. Raise the bar across the team. Mentor scientists, review designs, publish where the work merits it, and represent the science externally to customers and to the research community. A day in the life You might spend the morning in a design review arguing that a proposed approach won't survive contact with real data, the afternoon writing a prototype yourself to demonstrate the alternative, and the end of the day convincing an engineer that the capability is worth a sprint. Our sequencing is deliberate: try the idea on intuition, validate it on real data by inspection, then measure it, then operationalize it. Scientists here are expected to identify a problem, justify it, recruit others to it, and drive it into production, across whatever parts of the system that requires. Ownership follows the problem, not the org chart. About the team We are a combined science, product, and engineering team building one product together. Scientists own capabilities end to end rather than individual components, because these problems don't decompose cleanly: a single improvement typically touches extraction, storage, and retrieval at once. We invest in the tooling that makes that practical: local full-stack environments and sandboxed realistic data, so a scientist can go from idea to result in seconds rather than waiting on a deployment or on engineering support. The work is grounded in real usage rather than benchmarks alone, which is a rare combination for science this early: real users, real data, real feedback, and a genuinely unsolved research agenda.

Our team in Amazon Robotics builds robotic systems that perform contact-rich manipulation tasks safely and reliably in complex, unstructured environments — at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an Applied Scientist to join our Motion Behaviors team. You will drive the development of learned controllers and manipulation behaviors, applying techniques like reinforcement learning and behavior cloning to robots operating in Amazon fulfillment centers. These problems remain unsolved at scale: our robots must improve continuously in environments where simulation alone is insufficient. You will make principled decisions about when learned approaches should replace engineered solutions, and how to select behaviors based on estimated risk. You will collaborate across disciplines and leverage rich operational data to continuously improve system performance. Key job responsibilities • Develop learned controllers and manipulation behaviors, from research prototyping through deployment on production robots. • Research, design, and implement motion planning, control, and decision-making algorithms that improve the performance of deployed systems. • Design and deploy learning pipelines that take policies from simulation training to reliable, real-time execution on physical robots. • Develop models that predict manipulation outcomes and inform behavior selection under uncertainty. • Leverage operational data from deployed systems to systematically identify failure modes and drive policy improvements. • Represent Amazon in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that assist you and eligible family members, including domestic partners. 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!

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! We are looking for passionate, hard-working, and talented individuals to help us push the envelope of content localization. We are seeking scientists with experience in audio processing, speech/voice AI and machine learning. We work on a broad array of research areas and applications, including but not limited to multimodal machine translation, speech synthesis, speech analysis, and asset quality assessment. Candidates should be prepared to help drive innovation in one or more areas of machine learning, audio processing, and natural language understanding. If you have experience with speech synthesis and foundational models, then that's a huge plus! Key job responsibilities As an Applied Scientist, you should be a strong communicator, able to describe scientifically rigorous work to business stakeholders of varying levels of technical sophistication. You will closely partner with the solution development teams, and should be intensely curious about how the research is moving the needle for business. Strong inter-personal and mentoring skills to develop applied science talent in the team is another important requirement. - Lead research and development of speech and audio generation technology and end-to-end speech-to-speech architecture - Develop audio processing solutions for production environments, including source separation, enhancement, and mixing - Define the research roadmap for your area, identify high-impact problems, and communicate technical direction to senior leadership - Publish research, contribute to the broader scientific community, and bring external advances into production systems A day in the life You might start your morning reviewing experimental results and refining a model architecture before syncing with your engineering partners on integration plans. After lunch, you could be whiteboarding a new approach to a problem your team recently identified, then writing up findings for an internal science review. You will regularly present your work to peers and stakeholders, participate in code and design reviews, and explore emerging research that could unlock new possibilities for your team. About the team Our team is driven by a shared commitment to applying science in ways that create meaningful impact for customers. We value rigorous research, collaborative problem-solving, and a willingness to experiment with new ideas. You will work alongside talented scientists and engineers in an inclusive environment where your contributions shape the direction of our work. We are focused on building solutions that matter at scale, and we are looking for teammates who are energized by that challenge.

The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers on a mission to develop a fault-tolerant quantum computer. Throughout your internship journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of Quantum Computing and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for Quantum Research Science and Applied Science Internships in San Francisco, CA; Santa Clara, CA; Pasadena, CA; and Boston, MA. We are particularly interested in candidates with expertise in any of the following areas: superconducting qubits, cavity/circuit QED, quantum optics, open quantum systems, superconductivity, electromagnetic simulations of superconducting circuits, microwave engineering, benchmarking, quantum error correction, fabrication, etc. Key job responsibilities In this role, you will work alongside global experts to develop and implement novel, scalable solutions that advance the state-of-the-art in the areas of quantum computing. You will tackle challenging, groundbreaking research problems, work with leading edge technology, focus on highly targeted customer use-cases, and launch products that solve problems for Amazon customers. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex problems and to communicate research findings clearly. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. Critically review and validate outputs from AI tools and automated systems. 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. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Do you have a passion for data? Are you matriculating in a Master’s or PhD program? Amazon is looking for driven data science students with strong modeling skills who are comfortable owning and executing data. To be successful in this internship, you will need the ability to develop, automate, and run analytical models for our systems. During this internship, you will build tools and support structures needed to analyze and dive deep into data to resolve systems errors and changes. You will have the ability to present your findings to our business partners and help drive improvements. Previous applicants demonstrated the aptitude to manage medium-scale modeling projects, identified requirements, and built methodology/tools that were statistically grounded. For more information on the Amazon Science community please visit https://www.amazon.science

At Amazon, Security is a top priority. Are you interested in shaping the future of security for AWS, our customers, and the broader open source community? Do you want to be part of building something with massive impact? Our AWS Security Customer Outcomes Team is looking for a strong applied scientist as we help improve the security of open source software and the safety of the software supply chain generally. We're looking for an ambitious team member who will bring added scientific rigor and innovation to how our customer's experience security with and through open source software. Our systems operate on a global scale across a diverse ecosystem of field teams, security requirements, and tooling which requires high velocity security innovation that scales. We are seeking an Applied Scientist to help define, design, build, and operate AI-powered security solutions across the full lifecycle of open source software usage. You will work alongside a team to build intelligent automation that augments manual identification, analysis, and remediation workflows, enforces security controls for agents and human experts, and provides data-driven insights to AWS leadership. Key job responsibilities - Architect and build AI-powered security applications and tooling — Design and implement LLM-based systems (leveraging Bedrock, SageMaker, and state of the art patterns) for intelligent code scanning, automated solution development/deployment, and remediation alternatives. - Transform manual processes – Reimagine and deliver change from low throughput, human-dependent processes into high volume, machine speed pipelines without sacrificing key quality metrics. - Collaborate with and influence other leaders – The open source ecosystem has many stakeholders, and change requires listening and supporting but also influencing and leading through action. - Drive proactive security automation — Build systems that identify and remediate security issues in deliverables (code, threat models, content) without requiring explicit builder consent at every step, moving from reactive reviews to proactive prevention - Own operational excellence — Define and maintain SLAs, monitoring, alerting, runbooks, and incident response for services you own; participate in on-call rotation to support 24/7 availability - Lead technical design — Produce design documents, conduct trade-off analysis, and drive alignment across organizations. - Design and conduct scientific research using machine learning and deep learning techniques to address complex, ambiguous problems, working backwards from customer needs to invent new approaches or extend existing ones. - Build, train, and evaluate production-quality models using frameworks such as PyTorch or TensorFlow, applying rigorous experimentation, feature engineering, and hyperparameter optimization to improve accuracy and performance. - Write clean, maintainable code with optimal data structures and algorithms, ensuring your components integrate directly into production systems and meet high standards for operational reliability and resource efficiency. - Collaborate with engineering and product partners to translate scientific insights into scalable solutions, clearly communicating design decisions and trade-offs to ensure long-term maintainability. - Contribute to the scientific community by authoring or co-authoring peer-reviewed publications, mentoring less experienced scientists, and participating in technical reviews across teams. A day in the life You might start your morning reviewing experiment results and refining a model architecture before syncing with engineering partners on an upcoming production integration. After lunch, you could dive into a research paper relevant to a challenge your team is tackling, then prototype a new approach and set up evaluation pipelines. Later, you may join a design review to share your findings or pair with a teammate to debug a tricky data pipeline issue. Throughout, you balance hands-on scientific exploration with collaborative problem-solving. About the team At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Diverse Experiences Amazon Security 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 Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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.

The NASC & TOM Science team owns Operations Research, Machine Learning, and AI projects across the North America Sort Center (NASC) and Transportation Operations Management (TOM) planning and operations organizations. We turn complex network, labor, and capacity problems into deployed models that drive multi-million-dollar planning decisions every day. As a Data Scientist II, you will own the end-to-end Machine learning Operation cycle: Design, build, and ship machine learning and/or optimization models that directly shape Amazon's middle miles planning decisions. You will own end-to-end delivery — from problem framing with business partners, through modeling and validation, to deployment in internal model hosting platform and integration with downstream planning tools. You will work on problems such as: Long- and short-horizon forecasting Network and capacity optimization GenAI / agentic systems Defect prevention and adaptive planning You will partner closely with Engineering, Product, Engineering, and stakeholders to translate ambiguous operational pain points into measurable model outcomes. Key job responsibilities - Design and implement complex ML and optimization solutions (forecasting, MIP/LP, simulation, Deep learning / foundation model) - Drive end-to-end delivery of scalable models. From data exploration and feature engineering through training, evaluation, deployment, and post-launch monitoring; - Develop new modeling patterns and analytical frameworks for forecasting (multivariate, hierarchical, causal-DAG, model-chaining) and optimization; - Build robust model validation, backtesting, and monitoring pipelines; identify and eliminate sources of leakage, bias, and silent failure; - Define and own model performance metrics (e.g., WAPE) tied to business outcomes; - Partner with Data Engineering and Software Development to productionize models and define I/O contracts, packaging, and model CI/CD; - Excellent communication to present findings, tradeoffs, and recommendations clearly to stakeholders and senior leadership.

Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed, Sr. Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes. This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences

The Sponsored Products and Brands team at Amazon Ads is re-imagining advertising through generative AI technologies, transforming how millions of customers discover products and engage with brands across Amazon.com and beyond. We are bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle, from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities - Design and implement machine learning models and algorithms that power advertiser-facing AI experiences, ensuring scientific rigor from research through production deployment. - Conduct applied research to extend or invent new approaches for complex advertising problems where no textbook solutions exist, working backwards from advertiser needs. - Collaborate with engineering, product, and science teams to integrate your solutions directly into large-scale production systems serving millions of advertisers. - Analyze experimental results and system performance to identify improvement opportunities, making informed tradeoffs between model complexity, latency, and business impact. - Mentor other scientists, provide peer feedback on research procedures and results, and contribute to the team's scientific roadmap and technical direction. A day in the life You start your morning reviewing experiment results from a model you recently launched, checking metrics and identifying areas for iteration. By mid-morning you're whiteboarding a new approach with fellow scientists and engineers, debating tradeoffs between model architectures. After lunch you write and test code for a prototype, then join a design review where you share your findings and gather feedback. You wrap up by drafting a short document outlining next steps for a research proposal you plan to share with the broader team. About the team Our team within Amazon Ads builds AI-powered systems that help advertisers succeed at scale. We develop personalized, context-aware guidance tools grounded in large language models and advanced reasoning frameworks, connecting advertisers with actionable insights across multiple surfaces. Our mission is to make advertising simpler and more effective for businesses of all sizes. We value scientific creativity, collaborative problem-solving, and shipping real solutions that reach millions of advertisers worldwide.

What will customers want from Amazon Devices one, two, or three years from now? As a Data Scientist II on our Devices forecasting team, you will answer that question by building econometric and machine learning models that project long-term demand, assess the incrementality of new products, and quantify willingness to pay for specific features. Your analysis will directly shape portfolio decisions, helping product managers decide what to build next. This is a team that is investing in AI to accelerate how science informs business strategy, making now a particularly exciting time to join. Key job responsibilities - Build and validate econometric and machine learning models that generate long-term demand forecasts for Amazon Devices, selecting the right methodology based on data characteristics and business context. - Assess the incrementality of new products and quantify willingness to pay for product features, translating model outputs into clear narratives that help product managers adjust their portfolio strategy. - Collaborate with product managers, engineers, and business stakeholders to scope analytical projects, define metrics, and identify the data requirements needed to answer ambiguous forecasting questions. - Communicate findings to technical and non-technical audiences through clear documentation, effective visualizations, and well-structured presentations that drive informed decisions. - Mentor less experienced data scientists through code reviews, knowledge sharing, and active participation in scientific discussions and team planning. A day in the life You might spend your morning refining a demand forecast model, testing how a new product feature variable improves prediction accuracy. After lunch, you could be walking product managers through your incrementality analysis and aligning on what the numbers mean for their roadmap. Later, you might review a teammate's willingness-to-pay study or experiment with an AI-based approach to accelerate your modeling pipeline. Your work moves between deep independent analysis and collaborative sessions where you translate complex results into actionable recommendations. About the team Our team owns long-term forecasting and product analytics for Amazon Devices. We build the science that tells the story of where customer demand is headed and what drives it. You will work alongside scientists, engineers, and product managers who value rigorous analysis and practical impact. We are currently expanding our use of AI to accelerate how we deliver insights, and we are looking for people who are curious, collaborative, and ready to help shape that direction.