Careers

Do you want to create intelligent, adaptable robots with global impact? The Vulcan Stow team (https://www.amazon.science/latest-news/how-amazon-robotics-researchers-are-solving-a-beautiful-problem) at Amazon Robotics builds high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. We invent and deploy machine learning, optimization algorithms, and geometric reasoning models that empower robots to identify the best available placement opportunities, learn effective stow strategies, and continuously improve capacity utilization. Our mission is to deliver robust, real-time 3D scene understanding that drives downstream manipulation decisions - spanning semantic occupancy prediction, multi-view 3D reconstruction, depth estimation, and panoptic segmentation. We hire and develop subject matter experts in 3D computer vision, deep learning, and generative modeling, targeting high-impact algorithmic unlocks in scene completion, shape reasoning, and edge-optimized inference. We are seeking an experienced Applied Science Manager to lead the Perception team - a group of applied scientists and engineers pushing the frontier of 3D computer vision for robotics. You will drive technical vision using transformer-based architectures, generative 3D models, and scalable data pipelines to deliver sub-250ms perception outputs with the accuracy, robustness, and latency that production robots demand. Collaborating with cross-functional teams across match & affordances, motion planning, and fulfillment operations, you will ship models that run on real robots in real fulfillment centers. Key job responsibilities People Leadership: Prioritize being a great people manager - motivating, rewarding, and coaching your diverse team is the most important part of this role. Recruit and retain top talent in 3D computer vision, deep learning, and perception systems. Excel in day-to-day people and performance management tasks. Technical Vision: Set a vision for your team and create technical roadmaps focused on 3D scene understanding, semantic occupancy prediction, depth estimation, and multi-view fusion. Guide research, design, deployment, and evaluation of perception models - including encoder-decoder networks, query-based transformers, and generative architectures - that produce actionable 3D representations for downstream robot decision-making. Cross-functional Collaboration: Work closely with match & affordances, motion planning, hardware, and fulfillment teams to deliver integrated perception solutions that are robust to occlusion, clutter, and sensor noise. Partner with ML infrastructure teams on scalable training pipelines, pseudo-ground-truth data generation, and edge deployment optimization. Delivery Excellence: Implement best practices in applied research and software development. Manage project timelines, resources, and deliverables effectively. Keep technical skills current to contribute meaningfully to architecture decisions, model reviews, and inference optimization discussions. Problem Solving: Regularly participate in deep-dive troubleshooting exercises and drive technical post-mortem discussions to identify root causes of perception regressions, model failures, depth/segmentation misalignments, and latency degradations. A day in the life - Prioritize being a great people manager: motivating, rewarding, and coaching your diverse team is the most important part of this role. You will recruit and retain top talent and excel in people and performance management tasks. - Set a vision for the team and create the technical roadmap that deliver results for customers while thinking big for future applications. - Guide the research, design, deployment, and evaluation of complex computer vision and machine learning algorithms for contact-rich, cluttered, real-world manipulation problems. - Work closely with motion, hardware, and software teams to create integrated robotic solutions that are better than the sum of their parts. - Implement best practices in applied research and software development, managing project timelines, resources, and deliverables effectively. Amazon offers a full range of benefits for 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 Watch this video to learn more about Vulcan program in Amazon Robotics: https://www.amazon.science/latest-news/how-amazon-robotics-researchers-are-solving-a-beautiful-problem

At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research

We’re working to improve shopping on Amazon using the conversational capabilities of LLMs, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, across the breadth of Amazon Shopping and AGI to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!

How can we improve the customer experience by tailoring what we display on our pages based on available data? How do we build models that help us innovate in different ways to enhance customer experience? What is the relationship between what customers do on the site vs. what they actually buy? How do we do all of this without asking the customer a single question? Our team's stated missions is to "grow each customer’s relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations." Recommendations at Amazon is a way to help customers discover products. Our team strives to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations, and run these models directly on the retail website. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and the retail business and you will measure the impact using scientific tools. We are looking for a passionate, hard-working, and talented Applied Scientist who has experience building mission critical, high volume applications that customers love. You will have an opportunity to make an enormous impact on the design, architecture, and implementation of cutting edge products used everyday by people you know.

Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? Answer yes to any of these questions and you will be a great fit for our team at Amazon. As an Senior Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for Personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Key job responsibilities - Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to optimize the shopping experience, and run these models directly on the retail website. - Develop AI solutions for Recommendation systems using Deep learning, LLMs, Reinforcement Learning, distillation, and Optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field;

Are you excited to figure out not just what is happening but why — and to help build a delivery business that's still taking shape? We're looking for a Data Scientist who thrives on ambiguity and wants to own measurement, modeling, and experimentation across how customers experience Amazon's drone-delivery service. Your work will span the full data-science toolkit: designing and analyzing experiments (A/B tests), deep-diving customer-experience issues to find root causes, building propensity and behavioral models, forecasting demand, and applying causal methods to understand what actually drives our metrics. You'll work with large, evolving operational and customer datasets; partner closely with data engineers, scientists, and business stakeholders; and translate rigorous analysis into clear, decision-ready recommendations. Because the business is early and moving fast, you'll help define the right problems as much as solve them — with real room to explore new methods and shape how we measure and improve as we scale. If you're a curious, collaborative problem-solver who's energized by turning complex, ambiguous data into insight and clear direction, we'd love to hear from you. Key job responsibilities - Design and execute data science solutions using a range of methodologies—including machine learning, statistical modeling, and generative AI techniques—to address business problems where the approach is not immediately clear. - Acquire, transform, and validate large, evolving operational and customer datasets dive deep to investigate anomalies and data quality; and partner with data engineers to bring models and metrics into production. - Design, run, and analyze experiments (A/B and quasi-experimental studies) to measure impact, size opportunities, and guide product and operational decisions. - Deep-dive customer-experience issues and metric movements to identify root causes — the why behind the what, including how our metrics and their drivers relate and translate findings into clear, actionable recommendations. - Communicate complex analyses to technical and non-technical audiences, earn the trust of senior leaders, and influence roadmap and prioritization decisions with your recommendations. - Own your workstream end-to-end, from problem definition through delivery and ongoing measurement partnering across data engineering, product, and business teams as the business scales. A day in the life You might start your morning reviewing model performance metrics before joining a working session with engineers to refine a data pipeline. After lunch, you could be prototyping a new machine learning approach, running experiments, and comparing results against baseline models. Later, you might present preliminary findings to business partners, translating statistical outputs into plain-language recommendations. You will regularly participate in team discussions, scientific reviews, and mentoring conversations that keep you learning and growing.

We’re pioneering frontier science solutions to detect and treat product quality issues and enhance post-order experience in Amazon. Our goal is to enhance support for our diverse seller community and foster improved outcomes for both sellers and consumers for broader Amazon ecosystem. We are looking for passionate innovators who are excited about technology, driven by customer experience, and eager to make a lasting impact on the industry. In this role, you'll collaborate with top-tier scientists, engineers, and technical program managers (TPMs) to drive innovation in GenAI foundation models, adapt Large language model to our domain, develop efficient tabular foundation model, innovate on behavior foundation model. You will lead the effort to leverage Amazon's large-scale computing resources to accelerate advances in GenAI and frontier ML solutions. If you’re enthusiastic about joining a dynamic and motivated team, this is your chance to be part of an exciting journey. Apply now and help us shape the future of seller support at Amazon! Key job responsibilities 1. Develop domain-specific foundation models. 2. Apply the domain-specific foundation model to product risk detection, seller interactions capturing, future seller behaviors prediction, and seller responses simulation across varied conditions. 3. Work with business and engineers to develop and deploy the solutions.

The Amazon Travel & Events (AT&E) Technology Solutions team is looking for a Data Scientist to build intelligent, AI-driven solutions that transform how Amazon manages travel and events at scale. You'll develop machine learning and generative AI systems that improve the customer experience and deliver measurable business value for Amazon's business travelers and events programs. Working alongside experienced engineers, you'll advance conversational AI, intelligent automation, and data-driven decision making. You'll work hands-on with Amazon's heterogeneous travel data—contracts, booking systems, supplier data, and event logistics—using large-scale compute to accelerate travel and events intelligence. Key job responsibilities - Machine Learning Development — Design and implement advanced ML models and algorithms to solve complex problems across the travel and events domain - Production NLP Systems — Build and optimize large-scale NLP solutions using LLMs and transformer architectures, and take them to production - Applied GenAI — Adapt foundation models and LLM-based approaches to travel and events challenges through fine-tuning, prompt engineering, and Retrieval-Augmented Generation (RAG) - Data Pipeline Architecture — Build efficient, scalable data pipelines and architectures for AI applications on AWS - Cross-Functional Collaboration — Partner with Travel Operations, Supplier Category, and Travel Technology teams to support data-driven decisions and improve business processes - Experimentation and Analysis — Design and run rigorous experiments and ablation studies on large-scale datasets, and deliver results with statistical rigor - Code Quality — Write clean, well-tested, production-quality code and build ML pipelines from prototype through deployment - Applied Research — Track the latest research in GenAI, Vision-Language Models (VLMs), and multimodal AI, and identify where it can solve team problems

Pricing is one of the most consequential decisions Amazon makes — and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on hard causal identification problems in a high-stakes production environment — and who finds satisfaction in making rigorous methods accessible to a broader team — this role is for you. Key job responsibilities * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation

We are seeking an Applied Scientist to focus on Robot Navigation. In this role, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. Key job responsibilities - Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles - Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance - Collaborate with world model, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system - Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance - Mentor fellow scientists and engineers while maintaining strong individual technical contributions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products.