Careers

At Amazon, our SCOT Labs team owns and operates the experimentation platform that powers randomized controlled trials (RCTs) across Supply Chain Optimization Technologies (SCOT). We are the scientific gatekeepers for policy updates that govern how Amazon buys, stores, and moves billions of units of inventory worldwide. This is not traditional A/B testing: we are building the infrastructure and methodology to causally evaluate complex and interconnected supply chain interventions. Our platform runs experiments that span millions of products and hundreds of fulfillment nodes simultaneously, measuring the real-world impact of policy changes on inventory health, customer experience, and operational cost. We are also advancing the science of causal inference in supply chain settings by developing novel approaches to treatment effect estimation, interference modeling, and emulation techniques that allow us to assess policy impact faster and more accurately than ever before. The experiments you design and the methods you build here will directly determine which policies ship to production. These decisions influence hundreds of millions of dollars in weekly inventory investments, labor allocation for tens of thousands of associates, and Amazon's overall supply chain efficiency. Beyond operational impact, this team pushes the frontier of causal experimentation methodology and contributes to the broader scientific community with publications at top venues. If you are a scientist who wants to shape how one of the world's largest supply chains makes decisions — solving causal inference challenges in real-world settings no academic lab or startup can replicate — this is the team for you. Key job responsibilities - Partner with customer teams to design rigorous large-scale experiments (such as randomized controlled trials and quasi-experiments) to evaluate policy updates and model improvements across millions of products, hundreds of fulfillment nodes, and diverse business contexts - Lead the end-to-end experimentation lifecycle, from hypothesis formulation through analysis and stakeholder alignment, to inform production rollout decisions - Advance causal inference methodology for supply chain settings, including treatment effect estimation, interference modeling, and emulation techniques that accelerate policy evaluation - Build and maintain production-grade experimentation infrastructure and analytical tools using Python, SQL, Scala, and related technologies - Perform large-scale exploratory data analysis to uncover patterns, identify opportunities, and inform experimental design and policy development - Develop and scale supply chain emulation systems that model inventory dynamics end to end, enabling rapid offline evaluation of policy changes across millions of products without the cost and latency of live experiments - Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels - Contribute to Amazon's scientific community and the broader research field through collaboration and publication in top-tier venues A day in the life You might start the morning reviewing results from a randomized controlled trial running across millions of products, digging into causal estimates and designing the next iteration. Later, you could be designing an experiment with a partner team where interference is unavoidable: treated and control units share fulfillment networks and inventory pools, and you need a credible strategy despite the spillover effects. You'll build supply chain emulation systems that replicate inventory dynamics end to end, write code in Python, Scala, and SQL at a scale most scientists never encounter, and collaborate with scientists, engineers, and business teams across SCOT. Your research has a real chance of being published at top venues. The work is hard, the problems are unsolved, and the impact is immediate. If you want to do research that ships, this is where you do it. About the team The Forecasting and Labs Science team sits at the heart of Amazon's supply chain, building the science that determines what products are available, when, and at what cost for hundreds of millions of customers around the world. Our mission spans two deeply connected frontiers: pushing the boundaries of large-scale time series forecasting through foundation models that generalize across an enormous and diverse catalog of products, and building the experimentation and causal inference methodology that rigorously evaluates whether supply chain policy changes should ship to production. We are a team of scientists who care deeply about both research rigor and real-world outcomes. We don't just publish: we ship. And we don't just ship: we measure, iterate, and raise the bar. On the forecasting side, we build foundation models at a scale unmatched in industry, running experiments across millions of products and exploring novel data generation techniques that open new frontiers in model generalization. On the experimentation side, we design and run randomized controlled trials across hundreds of fulfillment nodes, advance causal inference in settings where interference is unavoidable, and build supply chain emulation systems that can evaluate policy changes in hours rather than months. Our work spans the full lifecycle: from foundational research and large-scale experimentation to production deployment and downstream impact measurement across supply chain, inventory, and financial planning.

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 next level. 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. As a Research Scientist, you will work with a unique and gifted team developing exciting products for consumers and collaborate with cross-functional teams. Our team rewards intellectual curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the intersection of both academic and applied research in this product area, you have the opportunity to work together with some of the most talented scientists, engineers, and product managers. 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.

The Amazon Web Services Center for Quantum Computing (CQC) in Pasadena, CA, is looking to hire an Applied Scientist. You will join a multi-disciplinary team of experimental and theoretical physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have a solid foundation in experimental physics and deep and broad knowledge of experimental measurement techniques. 

Candidates should have a track record of original scientific contributions, and those with deep knowledge and experience in low temperature condensed matter physics (superconductivity, Josephson junctions, phononics, TLS physics etc.) will be preferred. We are looking for candidates with strong engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. Working effectively within a team environment is essential. You will be expected to work on new ideas and stay abreast of the field of experimental quantum computation. Key job responsibilities In this role, you will be part of the Physical Metrology and Exploration (PME) subgroup within the Device team. You will work with the basic building blocks of quantum processors, ranging from superconducting films and resonators to junctions and qubits. There are two core missions for the PME team. The first is to support the CQC’s central activities through the development of devices, both existing and novel, that fall within this complexity range. This involves understanding their physical principles, identifying performance limitations, and collaboratively enhancing their capabilities. In many cases though, existing tools and measurement protocols fall short in delivering the depth of insights required for this first mission. Hence the second core mission of the team centers on physical metrology development. This involves continuously refining existing methodologies and simultaneously devising entirely new ones by identifying specific physical phenomena and exploiting them for measurement applications. You will constantly learn, innovate, carry out agile and yet scientifically rigorous research, and disseminate findings to the rest of the organization. You will be responsible for building experiments that encompass the integrated stack: design, fabrication, cryogenics, signal chain, and control stack software. Based on your research, you will provide recommendations that improve our next-generation quantum processors.

Payroll Tech's Sheriff team develops and maintains ML and Generative AI applications that support Payroll Operations and Amazon employees at scale. Our portfolio includes Pay-Input Anomaly Detection, which improves the pay experience by identifying pay input irregularities such as leaves and insurance discrepancies; Percept, which improves ticket resolution by providing intelligent ticket prioritization via sentiment scoring, ticket summarization, defect classification, and categorization; Penny, a Virtual Assistant that enables payroll operations teams to efficiently retrieve information from multiple sources including policies, Percept data, vendor data, and HR data via Xylem through a single browser interface; Pay Ticket Genie, in process of being integrated with Amazon AZA(A to Z Assistant); Niyam, our rule engine; and Policy as Code Extraction (PoCo), a critical component of SPACE (Single Payroll Autonomous and Computation Engine) Amazon's in-house payroll system built to eliminate third-party vendor dependency for payroll processing. PoCo ensures data accuracy by validating that pay instructions are correct and performing calculations when required. It consists of two components: policy-based rule creation, where business owners select a pay code and provide policy links to generate rules for specific business processes, and rule evaluation, where upstream services send real-time validation or calculation requests and receive results along with rationale for any failures. Sheriff team owns policy-based rule creation and powering the rule evaluation system with rules generated. As an Applied Scientist on the Sheriff team, you will own and advance the ML and GenAI capabilities that power these systems: driving model accuracy, scientific innovation, and global scale across the payroll ecosystem. Key Job Responsibilities As an Applied Scientist on the Sheriff team, you will operate across three core dimensions: Invent, Implement, and Influence. Invent You bring deep domain knowledge and fluency with state-of-the-art scientific approaches as well as emerging technologies from the research community. You practice customer-obsessed science : working backwards from the needs of Amazon employees and payroll operations teams to extend or invent new ML approaches, even when no textbook solution exists. You design novel ML and LLM-based methodologies for anomaly detection, sentiment analysis, ticket classification, prescriptive analysis, intelligent virtual assistance, and automated policy extraction. You identify and define the research agenda for expanding Percept's capabilities including prescriptive analysis feature; lead the scientific strategy for the Penny-AZA integration enabling accurate and low-latency responses to Amazon employee payroll queries, and drive the ML strategy for Policy as Code extraction(PoCo), developing models that extract, interpret, and codify payroll policies into structured, executable rules that power real-time pay instruction validation and calculation within SPACE. You author or co-author articles for internal or external peer-reviewed venues that validate the novelty of your work, when appropriate and not precluded by business considerations. Implement The ML components you develop are directly integrated into production systems or directly support large-scale applications serving Amazon's global payroll operations. You make appropriate tradeoffs between model accuracy and latency, innovation and stability, and immediate versus long-term solutions; favoring reuse and established frameworks where appropriate. You make progress semi-autonomously with only occasional guidance, implement at the correct level of complexity the first time, and evaluate emerging technologies including Large Language Models (LLMs) and GenAI frameworks largely on your own. You ensure your models and pipelines integrate robustly with data sources including USC (Unified Central Service), Xylem, SIM-Ticketing, Pay Code Governance system, and PoCo's rule evaluation engine. Influence You contribute to tactical and strategic planning for the Sheriff team, including goals, priorities, and roadmaps for ML and GenAI capabilities. You lead the scientific strategy for the global expansion of Percept, driving both feature growth and country-level launches own the complex AI track for Penny-AZA integration collaborating across partner teams including Reflect and GREF to ensure seamless data integration and robust ML pipeline workflows, and drive the scientific roadmap for PoCo's expansion to 100K US employees, ensuring the ML models powering policy extraction, rule generation and testing scale reliably to meet this growth. You mentor scientists and engineers on the team and across teams, championing best practices for the AI-Driven Development Life Cycle (AIDLC) to rapidly increase developer productivity and delivery velocity. You provide peer feedback on research procedures and results within and across teams, help recruit and develop bar-raising talent through interview drives, and grow the team's organizational knowledge of Sheriff team ML solutions. You are visible in the broader internal and external scientific communities as a subject matter expert and regularly serve as a Program Committee (PC) member at peer-reviewed conferences or review articles for journal publications.

Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA), you'll have the opportunity to reinvent the hiring process and deliver unprecedented scale, sophistication, and accuracy for Amazon Talent Acquisition operations. ITA is an industry-leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise. Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time. ITA stands at a critical juncture where our growth trajectory demands sophisticated analytical capabilities to revolutionize our hiring processes while raising the bar of candidates we hire. We are seeking an L5 Data Scientist to spearhead the next generation of Amazon's hiring practices through advanced scientific methods, focusing on two crucial domains - System Health Monitoring (SHM) and Select In for optimizing candidate quality .The role will be instrumental in driving a projected 60% reduction in defect detection time and contributing to ITA's ambitious 15% efficiency gain with Gen AI based workflow transformation target for 2026. The position will be pivotal in transforming our flagship recruiting system monitoring and extending sophisticated anomaly detection solutions across corporate hiring. By implementing advanced anomaly detection and root cause analysis, this role will directly impact our ability to provide reliable insights to talent acquisition customers while optimizing quality of hire. The role will be leading development of simulation models to identify how to attract and select in talent raising the bar of candidate quality in Amazon. The role will be directly responsible to The integration of cutting-edge Gen AI tools and multi-agent systems will revolutionize our scientific workflows, positioning ITA at the forefront of technological innovation in hiring practices. The criticality of this Data Scientist role cannot be overstated- Without this position, ITA cannot advance its primary objective of enhanced candidate evaluation, risking system downtime across four critical platforms, compromising candidate quality standards, and directly impacting Amazon's Quality of Hire strategy. Key job responsibilities The DS will be : 1. Partnering with senior DS and Applied scientists to identify customer pain points and transform them to measurable goals 2. Iterate rapidly prototypes on anomaly detection, agent based root cause analysis on anomalies detected using scientific approaches 3. Deploy scalable ML/science based models using AWS infrastructure 4. Identifying proactively areas of high value repeated work /product opportunities as candidates for automation using Gen AI 5. Design and run experiments on systems stability pre and post interventions 6. Follow agile workflow for daily project execution 7. Develop in partnership with economists and other DS an integrated framework for developing strategies that attract top tier talent for Amazon.

Amazon Health Services (One Medical) About Us: At Health AI, we're revolutionizing healthcare delivery through innovative AI-enabled solutions. As part of Amazon Health Services and One Medical, we're on a mission to make quality healthcare more accessible while improving patient outcomes. Our work directly impacts millions of lives by empowering patients and enabling healthcare providers to deliver more meaningful care. Role Overview: We're seeking an Applied Scientist to join our dynamic team in building state of the art AI/ML solutions for healthcare. This role offers a unique opportunity to work at the intersection of artificial intelligence and healthcare, developing solutions that will shape the future of medical services delivery. Key job responsibilities • Lead end-to-end development of AI/ML solutions for Amazon Health organization, including Amazon Pharmacy and One Medical • Research, design, and implement state-of-the-art machine learning models, with a focus on Large Language Models (LLMs) and Visual Language Models (VLMs) • Optimize and fine-tune models for production deployment, including model distillation for improved latency • Drive scientific innovation while maintaining a strong focus on practical business outcomes • Collaborate with cross-functional teams to translate complex technical solutions into tangible customer benefits • Contribute to the broader Amazon Health scientific community and help shape our technical roadmap

Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, State-of-the-art technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.

We are looking for a Senior Applied Scientist to join the Robotics Simulation team at Amazon Robotics. This role combines deep traditional robotics expertise (kinematics, dynamics, control, motion planning) with fluency in modern Physical AI approaches (imitation learning, vision-language-action models, world models, diffusion policies). You will be the technical anchor who bridges the gap between what works in simulation and what works on real robots. You will mentor junior scientists building RL and imitation learning pipelines, provide hands-on technical direction on sim-to-real transfer, foresee pitfalls in robot learning workflows before they become blockers, and drive the robotics development methodology for the team. This is not a pure research role: you will work directly with real robot hardware, simulation environments, and production deployment pipelines, ensuring that learned policies transfer reliably from GPU-accelerated simulation to physical robots operating in Amazon fulfillment centers. While this role is expected to engage with the research community and may produce publications, the primary measure of success is deployed robot capability, not paper count. We value scientists who ship. Key job responsibilities * Provide technical robotics direction for the team's Physical AI program, spanning simulation environment design, policy training, sim-to-real transfer, and real-world validation across multiple robotics platforms. * Mentor junior applied scientists and engineers on robot learning best practices, helping them diagnose sim-to-real gaps, debug policy failures on hardware, and iterate toward deployable solutions. * Design and execute sim-to-real transfer strategies, including system identification, domain randomization, physics parameter tuning, and visual domain adaptation, drawing on both classical and learned approaches. * Architect policy training pipelines that combine teleoperation data, synthetic demonstrations, reinforcement learning, and imitation learning (e.g., VLA models, diffusion policies, behavior cloning) for manipulation tasks. * Lead sim-to-real analysis: define metrics and methodologies for evaluating simulation fidelity, identifying where simulation diverges from reality, and prioritizing modeling improvements that impact downstream policy performance. * Collaborate with hardware teams on robot embodiment modeling, ensuring that digital twins accurately capture kinematics, joint dynamics, actuator limits, contact behavior, and sensor characteristics. * Evaluate and integrate state-of-the-art approaches from the Physical AI research community (foundation models for robotics, world models, action-chunking transformers, generalist policies) into the team's simulation and training infrastructure. * Contribute to end-effector modeling and physics tuning, ensuring physically plausible contact interactions and accurate tool behavior in simulation across diverse manipulation hardware. * Drive technical design reviews, author high-level design documents, and set the scientific direction for simulation fidelity and robot learning initiatives. 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 The Robotics Simulation team is a ~30-person multidisciplinary organization of SDEs, Applied Scientists, and Technical Artists at Amazon Robotics. We build the simulation infrastructure that powers Physical AI development, from photorealistic synthetic data to GPU-accelerated training environments. Our simulation infrastructure enables robots to be designed, trained, and validated entirely in simulation before physical hardware exists, compressing development timelines and de-risking hardware programs across Amazon Robotics. The team currently delivers end-to-end simulation stacks for Amazon's robotics programs, including high-fidelity robot digital twins, teleoperation data collection infrastructure, scalable synthetic demonstration generation, VLA/diffusion policy training and inference pipelines, domain randomization for visual and physics sim-to-real transfer, and model validation in simulation. We partner closely with hardware teams, science organizations, and robotics program leads across Amazon Robotics. This role reports into the Robotics Simulation organization and carries technical influence across broader partner teams (hardware engineering, science organizations, robotics program leads). You will not only shape direction within the simulation team but serve as the technical bridge connecting simulation fidelity decisions to real-world robot performance across multiple programs.

Economists on the Seller Fee Science team design scalable strategies and technology for the fees Amazon charges to third-party sellers, world wide. Our challenge is to facilitate continued improvement in both customer experience (selection, prices, delivery speed) and selling partner growth and innovation, while optimizing for Amazon's long-term profits. Key job responsibilities Design and develop models to assess the causal impact of fees, and fee-related policy on third party sellers’ behavior and business performance. Lead enhancements into existing fee calculation models to maximize the long term health of the Amazon third-party marketplace. Own the scientific vision and direction related to fees worldwide. Collaborate with product managers, data scientists, and software developers to incorporate models into production processes and influence senior leaders. Act as an ambassador of our team in the broader scientific community.

Amazon's Compliance and Safety Services (CoSS) Team is looking for a smart and creative Applied Scientist to apply and extend state-of-the-art research in NLP, multi-modal modeling, domain adaptation, continuous learning and large language model to join the Applied Science team. At Amazon, we are working to be the most customer-centric company on earth. Millions of customers trust us to ensure a safe shopping experience. This is an exciting and challenging position to drive research that will shape new ML solutions for product compliance and safety around the globe in order to achieve best-in-class, company-wide standards around product assurance. You will research on large amounts of tabular, textual, and product image data from product detail pages, selling partner details and customer feedback, evaluate state-of-the-art algorithms and frameworks, and develop new algorithms to improve safety and compliance mechanisms. You will partner with engineers, technical program managers and product managers to design new ML solutions implemented across the entire Amazon product catalog. Key job responsibilities As an Applied Scientist on our team, you will: - Research and Evaluate state-of-the-art algorithms in NLP, multi-modal modeling, domain adaptation, continuous learning and large language model. - Design new algorithms that improve on the state-of-the-art to drive business impact, such as synthetic data generation, active learning, grounding LLMs for business use cases - Design and plan collection of new labels and audit mechanisms to develop better approaches that will further improve product assurance and customer trust. - Analyze and convey results to stakeholders and contribute to the research and product roadmap. - Collaborate with other scientists, engineers, product managers, and business teams to creatively solve problems, measure and estimate risks, and constructively critique peer research - Consult with engineering teams to design data and modeling pipelines which successfully interface with new and existing software - Publish research publications at internal and external venues. About the team The science team delivers custom state-of-the-art algorithms for image and document understanding. The team specializes in developing machine learning solutions to advance compliance capabilities. Their research contributions span multiple domains including multi-modal modeling, unstructured data matching, text extraction from visual documents, and anomaly detection, with findings regularly published in academic venues.