Careers

Are you excited about leading a team of scientists to solve complex, high-impact problems using machine learning and data-driven approaches? At Amazon Advertising Sales, we are looking for an experienced Applied Science Manager who can build, grow, and guide a team that delivers research and production-quality models at scale. In this role, you will own the strategic direction of your team's scientific roadmap, partner closely with business and technical leaders, and ensure your team's work directly shapes important decisions and customer-facing products. You will bridge the gap between science, technology, and business — translating ambiguous challenges into clear research agendas and actionable results. We are specifically looking to build AI agents that can operate accurately at large scale, helping transform the way account teams operate and optimize their end-to-end workflows. We are looking for a deeply scientific leader that brings their expertise in Natural Language Processing, Large Language Models, Reinforcement Learning, Deep learning and/or Recommender Systems to bring exciting new products come to life. If you are a thoughtful, collaborative leader who thrives on turning scientific innovation into measurable impact, we want to hear from you. Key job responsibilities - Lead and manage a team of applied scientists and analysts, setting the strategic direction and roadmap for scientific research that influences organizational goals and annual planning processes. - Partner with business and technical stakeholders to define vision, priorities, and success criteria — ensuring your team builds the right solutions at the right level of fidelity and that final outputs are ready to inform business decisions or move to production. - Evaluate and improve machine learning model accuracy and performance using rigorous experimentation, feature engineering, and hyperparameter optimization, while establishing a team culture focused on reproducibility and scientific rigor. - Hire, develop, and mentor scientists and technical contributors, providing growth opportunities and empowering team members to take ownership of key workstreams and deliver results independently. - Identify opportunities for new analysis, efficiency improvements, and generative AI integration, allocating resources effectively and proactively mitigating risks before they become roadblocks. A day in the life You start your morning reviewing experiment results with your scientists, asking probing questions about model assumptions and business relevance. Mid-morning, you join a cross-functional meeting with engineering and product partners to align on priorities for an upcoming launch. After lunch, you conduct a one-on-one with a team member, coaching them on a new research proposal. Later, you draft a narrative summarizing your team's quarterly progress and outline next steps for leadership review. Throughout, you balance hands-on technical guidance with strategic planning to keep your team delivering high-quality science. About the team Sales AI is focused on applying scientific research and machine learning to solve real problems that matter to Amazon and its advertising customers. We value intellectual curiosity, collaboration, and a commitment to building inclusive, high-performing teams. We believe that great science happens when people with different perspectives work together toward a shared mission. As we continue to grow, you will play a key role in shaping the team's direction, expanding our capabilities, and ensuring our work has lasting impact across the business.

Ready to Transform Amazon's Procurement Systems? Are you passionate about building scalable solutions that impact how Amazonians get their work done? Join us in revolutionizing Amazon's procurement technology landscape through advanced analytics and machine learning. We're seeking a talented Data Scientist to help build the next generation of procurement applications from the ground up. This is your opportunity to work on high-impact systems that combine advanced search capabilities, AI-powered assistance. About This Role As a Data Scientist in the Global Corporate Procurement (GCP) Tech team, you'll be instrumental in building modern, AI/ML-powered applications that transform Amazon's procurement systems. You'll work on a greenfield initiative to create innovative solutions leveraging machine learning, natural language processing, and predictive analytics that make procurement faster, smarter, and more intuitive for Amazonians worldwide. This role offers the unique opportunity to develop data-driven systems leveraging AI/ML services, modern statistical modeling techniques, and generative AI capabilities. You'll collaborate with experienced data scientists, engineers, and cross-functional partners to deliver solutions that will eventually serve Amazon's global workforce. What You'll Do You'll design, develop, and deploy scalable machine learning models and data pipelines that power Amazon's procurement ecosystem. Working in an agile environment, you'll take ownership of analytical solutions from conception through production deployment, contributing to technical decisions alongside experienced data scientists and engineers. Your work will directly impact the daily experience of Amazonians as they procure goods and services. Through collaboration with product managers, UX designers, and fellow data scientists, you'll translate complex business requirements into elegant data-driven solutions using advanced analytics, NLP, and AI/ML techniques. Key job responsibilities * Use data analyses and statistical techniques to develop solutions to improve customer experience and to guide business decision making * Identify predictors and causes of business-related problems and implement novel approaches related to forecasting and prediction * Identify, develop, manage, and execute analyses to uncover areas of opportunity and present written business recommendations * Collaborate with multiple teams as a leader of quantitative analysis and where you develop solutions that utilize the highest standards of analytical rigor and data integrity * Analyze and solve business problems at their root

Amazon's Stores Economics and Science (SEAS) team is looking for an Applied Scientist III to help us find global optima where others settle for local ones. You will work alongside scientists and engineers with backgrounds spanning machine learning, natural language processing, information retrieval, statistics, economics, causal inference, and optimization to lower cost-to-serve, optimize selection, and apply emerging machine learning techniques across a wide range of Stores problems — all at a scale that directly shapes how customers shop. This is a role for someone who does not default to the most popular model but instead thinks carefully about which approach will produce the most reliable and scalable result for each problem. You will lead science initiatives end-to-end, translating complex business problems into mathematical frameworks, building large-scale algorithms, and deploying production solutions in partnership with product teams. If you want to do cross-discipline applied science that moves real metrics, we would like to talk. Key job responsibilities - Lead large-scale science initiatives from research through production, translating supply chain and marketplace problems into mathematical frameworks and deploying algorithms that delight Amazon customers. - Design and implement machine learning and statistical models — choosing the right method for each problem rather than defaulting to a single paradigm — and take ownership of their performance in production. - Drive your team's scientific agenda by identifying new research opportunities, proposing initiatives, and aligning with technical leadership on priorities. - Collaborate with scientists, engineers, and product teams across Amazon's Stores organization to prototype, validate, and scale solutions that accelerate partner teams' progress. - Mentor and coach fellow scientists through code reviews, design reviews, and authoring of internal technical documents and external publications. A day in the life You might start your morning analyzing experiment results from a model you recently deployed, deciding whether the estimates are robust enough to move to an online test. By mid-morning you are whiteboarding a new formulation with a colleague from a partner team. After lunch you review a pull request, suggesting a simpler algorithmic approach that cuts latency. Later you carve out time to write code for a prototype that tests a new idea for reducing cost-to-serve. Your work regularly moves between hands-on modeling and the collaborative work of aligning your team around a shared scientific roadmap. About the team Stores Economics and Science (SEAS) is an interdisciplinary team within Amazon's Stores organization. Our mission is to apply science and engineering to move from local to global optima in methods, models, and software. We prove concepts at small scale first, then build solutions that work at Amazon scale — and we help partner teams across the company short-circuit months of research and development. Our team includes a high concentration of Amazon Scholars, and we actively collaborate with academia. We value intellectual curiosity, open collaboration, and thoughtful problem-solving. We encourage publishing, invest in mentorship, and support your growth as both a researcher and a builder.

We are looking for an Applied Science Manager III to lead a team of applied scientists building next-generation AI-powered seller risk detection systems. This role sits at the intersection of deep technical science, strategic planning, and cross-organizational partnership. GRIP Science serves a complex stakeholder landscape: enforcement teams, policy owners, marketplace operations, peer science orgs, and senior leadership, each with distinct timelines and priorities. The right leader for this role will navigate that landscape with clarity, balancing urgent operational needs against the long-term scientific investments required to stay ahead of adversarial actors at scale. You will independently own the vision, roadmap, and execution for a critical detection surface, defining what to build, what to defer, and how to measure success. You will set and maintain a high bar for scientific rigor while cultivating a team culture that rewards creative problem-solving and principled risk-taking. Key job responsibilities Strategy & Stakeholder Partnership • Define the scientific vision and multi-year roadmap for your team's detection domain, ensuring alignment across a broad set of stakeholders with competing priorities and timelines • Develop strong working relationships with enforcement, policy, operations, and engineering partners; serve as the trusted scientific voice in cross-functional discussions • Exercise judgment in sequencing work. Distinguish between high-urgency requests that warrant immediate response and those better addressed through systematic, durable solutions • Influence org-level planning (OP1/OP2) through compelling written narratives and data-driven proposals that articulate both near-term deliverables and long-term scientific bets • Proactively communicate trade-offs to leadership and stakeholders, building trust through transparency about what your team will and will not prioritize in a given cycle Science & Technical Leadership • Lead a team building state-of-the-art detection models that leverage modern AI techniques, including deep learning, foundation models, and generative AI, to identify novel abuse patterns at scale • Set and uphold a high standard for scientific methodology: model validation, experimentation rigor, reproducibility, and interpretability of results • Evaluate end-to-end scientific proposals for soundness, scalability, and production readiness; know when to push for deeper investigation and when to ship • Foster a culture of scientific curiosity and invention. Encourage the team to challenge assumptions, explore out of the box approaches, and invest in research that creates step-function improvements rather than incremental gains • Identify where emerging AI capabilities (e.g., large language models, multimodal reasoning, self-supervised learning) can be applied to unlock new detection signals or fundamentally reshape existing pipelines • Make informed trade-offs between model sophistication, delivery timelines, and operational sustainability Execution & Operational Excellence • Scope scientific projects, set priorities, and drive schedules; readjust decisively when incoming signals warrant a change in direction • Structure work to reduce exposure to classic failure modes: ambiguous requirements, untested assumptions, under-instrumented models • Establish mechanisms and metrics to continuously measure model effectiveness, enforcement precision, and downstream business outcomes • Drive production readiness: monitoring, alerting, on-call practices, and rapid response for AI/ML systems processing millions of daily decisions Team Building & Development • Build and lead a high-performing team of applied scientists; attract and develop talent that combines technical depth with ownership mindset • Invest in the growth of individual scientists through deliberate project assignment, regular coaching, and actionable feedback tied to career progression • Create an environment where scientists have the space to do deep work, the support to take calculated risks, and the recognition when they deliver • Manage performance across the full spectrum. Accelerate top performers toward promotion readiness while addressing gaps constructively and with clear expectations About the team GRIP Science (Global Risk Intelligence & Prevention) is the core detection engine within TSI Science. We build and operate state-of-the-art ML and AI models that identify and block bad sellers across Amazon's 24 global marketplaces. Our systems cover the full seller journey, from registration through every action on the marketplace, and score holistic risk across all active sellers. We push the frontier of adversarial detection by combining large-scale deep learning, graph-based reasoning, generative AI, and real-time inference to outpace bad actors who continuously evolve their tactics. This is one of the most technically demanding and high-impact applied science problem spaces at Amazon. Your models directly shape marketplace integrity for hundreds of millions of customers worldwide.

Security is moving from something builders stop for to something that runs quietly inside how they already work: guidance that shows up in their tools as they code, and continuous checks that replace the weeks-long review queue. Behind that shift is data. Knowing which controls a service actually needs, surfacing where real risk lives across a massive portfolio, whether the guidance we give is accurate enough to trust, and whether any of it is genuinely reducing risk. You'll lead the data science and data engineering team that answers those questions and makes that whole model measurable. Proving efficacy at scale is where you'll have the biggest impact. It's easy to ship guidance, but hard to know it's working: that it's accurate, that builders act on it, that fewer issues reach production because of it. You'll own the data that turns those into answers leaders can act on, and the feedback loop that uses every issue we find to make the guidance smarter so the same problem never recurs. You'll be the leader who gets the craft of data scientists, data engineers, and business intelligence engineers, and can bring them together as one group with a shared direction. You'll set the vision, raise the technical bar, and build the foundations good science depends on: trusted data, reliable pipelines, and reproducible research. You'll love this role if you enjoy: - Turning security from a gate into a default, and proving with data that the shift actually works. - Finding where risk really lives, using data and models to surface the issues and high-risk code that matter most before they reach production. - Answering hard measurement questions at scale: is our guidance accurate, are builders acting on it, are fewer issues reaching production? - Working across disciplines, using science, analytics, and engineering together so each makes the others stronger. - Standing up something new: shaping the strategy, culture, and standards for a team ready for its first dedicated data leader. - Growing engineers and scientists and watching them take on bigger scope because of how you invested in them. Key job responsibilities - Set the strategy, roadmap, and technical direction for a combined data science and data engineering team, turning an ambiguous, fast-moving security mission into clear, prioritized work and partnering with tech and business leadership to shape the vision. - Own how we measure whether embedded security is working: build the metrics, experiments, and analysis that show whether guidance is accurate, whether builders act on it, and whether fewer issues reach production because of it. - Lead the data science behind risk identification and the compounding feedback loop: surface where real risk lives across a large portfolio, scope which controls each service needs, and make sure every issue found feeds back into smarter guidance so the same problem does not recur. - Determine the right mix of science, data engineering, and BI resources, use those disciplines to complement rather than duplicate one another, and lead senior individual contributors credibly across both the science and engineering sides of the team. - Hold the bar on quality and delivery: judge whether outputs are trustworthy and ready to inform decisions or ship, without letting work be under-scoped or over-engineered, and invest in the data foundation of trusted data, reliable pipelines, and reproducible research. Own the results and credit the people who deliver them. - Grow the team and manage ambiguity: develop scientists and engineers into bigger scope, support in-flight discipline transitions, establish the shared standards and data culture that let the team deliver without you, and mitigate risks and escalate with good judgment before they become roadblocks. About the team 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.

Amazon Robotics is transforming warehouse automation through edge AI and machine learning applied to real-world robotics challenges. We're seeking a Research Scientist to advance our mobile manipulation capabilities by developing novel learning-based approaches that enable robots to navigate and manipulate objects in dynamic fulfillment environments. This role offers the opportunity to conduct original research and translate state-of-the-art findings into production systems operating at Amazon's unprecedented scale. Key job responsibilities Research and Algorithm Development: Formulate novel research problems in robot learning and manipulation, design new model architectures, validate hypotheses through rigorous experimentation, and advance the state of the art in learning-based robotics. Data Strategy and Pipeline Design: Define data requirements for research initiatives, design scalable collection and curation strategies, establish governance and provenance standards, and build reusable pipelines ensuring data quality and reproducibility. Experimentation and Scientific Validation: Design and execute experiments in simulation and real-world embodiments, develop evaluation methodologies and benchmarks, perform ablation studies and statistical analyses, and iterate systematically to advance model performance. Prototyping and Research Infrastructure: Develop clean, well-documented research codebases, build experimentation frameworks and evaluation tooling, contribute to shared training infrastructure, and implement interfaces for broader robotics integration. Scientific Leadership and Publication: Drive an independent research agenda aligned with team objectives, publish at top-tier venues (e.g., RSS, CoRL, ICRA, NeurIPS), identify research gaps through literature reviews, and present findings via technical reports and talks. Cross-Functional Collaboration: Partner with scientists, engineers, and leaders across teams to translate research into deployable solutions, mentor junior researchers, contribute to the team's scientific culture, and support integration with robotics hardware teams. 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! About the team Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun!

We are seeking an Applied Scientist to contribute to the research and development of novel security validation and monitoring techniques for AI systems at scale. You will own and contribute to four critical work-streams: 1. Real-Time Agent Monitoring Design and implement scientific approaches for continuous behavioral analysis of AI agents in production—detecting anomalous actions, prompt injection exploitation, and policy violations in real time. 2. Protection & Automated Remediation Invent and deliver novel protection technologies and automated remediation techniques, building on research in security, cryptography, privacy, automated reasoning, and other domains, to enable safe and secure agentic AI models and AI applications. 3. AI Application and Capabilities Validation Invent and deliver scalable methodologies for security testing of AI applications and AI capabilities (e.g. MCP, skills), including adversarial robustness evaluation, safety guardrail bypass detection, tool-use authorization boundaries, and trust boundary verification. 4. AI Asset Discovery & Inventory Research and build scalable techniques to discover, identify, and catalog automatically all AI-enabled applications, services, and capabilities across the company—maintaining a comprehensive, continuously updated database of AI assets. Key job responsibilities Key job responsibilities Invent • Identify and frame new research challenges in AI security where problems are ill-defined and require novel scientific paradigms at the product level. • Contribute to the team's scientific agenda for agent monitoring, protection, remediation, validation research, and AI asset discovery. • Publish research results at peer-reviewed internal and external venues (e.g., USENIX Security, ACM CCS, IEEE S&P, NeurIPS, ICML security workshops, ICSE, PETS) when appropriate. • Articulate key scientific challenges of current and future AI security threats and deliver novel research to address them. • Design, implementation, and successful delivery of scientifically complex security solutions into production—both brand new systems and evolutions of existing ones. • Write significant portions of critical-path code for detection models, validation engines, protection technologies, and asset discovery / classification systems. • Assess and select appropriate technologies (e.g., data protection, private inference, graph-based anomaly detection, NLP-based service classification, code/traffic analysis for AI fingerprinting) for production systems. • Use best practices in scientific methodology and software engineering across the team; provide insightful peer reviews of code, design, and architecture artifacts. • Deliver solutions that are inventive, maintainable, scalable, and extensible. Influence • Autonomously drive discussions with security engineers, software engineers, product managers, and scientist peers across multiple teams. • Build consensus on larger cross-team security initiatives and factor complex efforts into independent workstreams. • Identify and resolve endemic problems, including areas where current security tooling limits innovation of partner teams. • Contribute to the broader internal and external scientific communities as a subject matter expert in AI security. About the team Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all 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 Foundational AI (FAI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs). Key job responsibilities As an Applied Scientist with the FAI team, you will support the development of RL Gyms, assess their usefulness for the frontier model advancement and build techniques to advance the state of the art with LLMs. You will support the foundational model development in an applied research role. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. A day in the life You start your morning reviewing experimental results from a model training run you kicked off the previous afternoon, analyzing metrics and refining your approach. By mid-morning, you are whiteboarding a new algorithmic design with engineering partners, working through system integration details. After lunch, you lead a science review where you present findings and gather feedback from peers. Later, you pair with a junior scientist to debug a data pipeline issue, turning it into a coaching moment. Your day wraps up drafting a section of a research paper capturing your team's latest results. About the team The FAI team has a mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.

Work on 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 Applied Scientist II, 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.

Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as an Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. The position is based in Seattle but will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, emerging technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Please visit https://www.amazon.science for more information Key job responsibilities Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the international seller services domain. 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 NLP techniques and methodologies to improve the accuracy and efficiency of language-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. A day in the life Push the boundaries of applied science - fine-tune large language models and develop novel NLP techniques to crack complex challenges in seller acquisition, content generation, and catalog understanding Work with data at massive scale - tap into some of the richest e-commerce datasets in the world to uncover patterns, generate insights, and drive real business impact Turn research into reality - prototype bold ideas, then partner with engineers to bring your models into production, balancing scientific rigor with real-world scalability Think globally, deliver worldwide - collaborate with leaders and teams across Europe, Japan, China, and Australia to build solutions that generalize across international marketplaces Solve problems that matter - translate ambiguous business challenges into well-scoped science problems that directly serve sellers and customers around the globe Collaborate with the best - engage in design reviews, contribute to technical thought leadership, and learn from a diverse community of world-class scientists and engineers Never stop learning - stay at the frontier of NLP, LLMs, and applied ML, bringing the latest research advances into your work Own your impact - operate with a customer-obsessed mindset where every model you build helps sellers thrive and expands selection for customers worldwide