Careers

Have you ever placed an order on Amazon and wondered how it got to you so fast? Behind that speed is a massive transportation network generating billions of data points daily. We need someone who can turn that data into clarity. Come join the Network Engineering, Scheduling and Technology (NEST) Science team within Amazon Transportation Services. We are looking for a Data Scientist who is equal parts data engineer, visualization architect, and analytical modeler. You will own the end-to-end build process for data-driven solutions: identifying business needs, developing simulation and optimization models, building computationally efficient analytical tools, and narrating results through compelling data storytelling. This is not a dashboard-building role. You will work at the intersection of large-scale data processing, advanced analytics (including simulation and optimization), and data visualization, building tools that allow stakeholders to explore millions of records interactively, uncover patterns in network performance, and make data-driven decisions with confidence. The ideal candidate is a data wizard who thrives on wrangling massive datasets, building predictive and prescriptive models, architecting performant query and aggregation pipelines, and crafting visualizations that communicate complex findings with precision and clarity. You will own the full lifecycle, from problem identification and data extraction through modeling and simulation to production-grade analytical applications that narrate results back to stakeholders. You will collaborate closely with scientists, engineers, and product managers Key job responsibilities - Own the end-to-end analytical lifecycle: identify stakeholder needs, frame problems, build models, and narrate results through data tools and visualizations - Design and build production-grade analytical tools, BI applications, and interactive data products that enable self-service exploration of very large transportation datasets (billions of records) - Develop and enhance simulation and optimization models (discrete event simulation, agent-based modeling, mathematical optimization) applied to network planning and transportation operations - Architect computationally efficient data pipelines and aggregation strategies that support responsive, real-time or near-real-time visualization at scale - Develop advanced data storytelling artifacts that communicate complex network dynamics, trends, and anomalies to technical and non-technical stakeholders - Build and maintain reusable visualization frameworks and libraries tailored to transportation network data (routing, scheduling, flow, capacity) - Work with large-scale data platforms (Redshift, Spark, S3, Athena) to extract, transform, and model data for analytical consumption - Develop code (Python, SQL, Scala) for data processing, statistical modeling, simulation, and building automated analytical workflows - Collaborate with Applied Scientists, Research Scientists, Software Engineers, and Product Managers to integrate analytical tools into broader planning and decision-support systems - Define and implement best practices for data visualization performance, including sampling strategies, level-of-detail rendering, and progressive loading for large datasets - Communicate findings, methodology, and recommendations through compelling written and verbal presentations to leadership and business customers About the team The Network Engineering, Scheduling, and Technology (NEST) Science Team prototype, build, and productionize mathematical models that reduce transportation cost and improve customer experience in Amazon's Middle Mile network. Equipped with techniques from Operations Research, Machine Learning and Simulation, these models are used to govern scheduling and equipment selection of hundreds of thousands of truck movements, optimize network configurations, determine the transit times between nodes, and simulate network flow under uncertainty for informed decision making. Our core team consists of Applied, Data, and Research Scientists along with technical Product Managers that come from diverse backgrounds.

Amazon serves hundreds of millions of customers. Each one has a unique history of purchases, preferences, and behaviors. Our team's mission: turn that history into real-time contextual intelligence that makes every Amazon experience feel personal. We're hiring an Applied Scientist to push the boundaries of what's possible with LLMs, semantic retrieval, and customer understanding at scale. The problem space: Imagine a system that can instantly synthesize years of customer signals — what they bought, what they love, what they're planning — and surface the exact right context for any experience, in milliseconds. That's what we build. It's equal parts information retrieval, generative AI, and systems engineering. Why this role: 1. Scale: Your models will serve 1,500+ requests per second across Amazon's largest surfaces. 2. Impact: Direct revenue attribution in the hundreds of millions — your work shows up in customer experiences the same week. 3. Frontier tech: Fine-tuning LLMs, building custom embedding models, designing retrieval architectures that balance quality with sub-100ms latency constraints. 4. Data richness: Access to one of the most comprehensive customer behavior datasets anywhere. 5. Ownership: End-to-end — from research to production deployment to metric evaluation. Key job responsibilities 1. Invent new approaches to contextual retrieval, relevance scoring, and LLM-based summarization. 2. Fine-tune and evaluate language models for domain-specific understanding. 3. Design experiments that measure real customer impact, not just benchmark scores. 4. Ship production systems and iterate based on live metrics. 5. Collaborate across teams — Alexa, Search, Recommendations — as a platform that powers them all. 6. Mentor team members and shape the technical direction of our roadmap. Please visit https://www.amazon.science for more information. A day in the life You'll analyze large-scale behavioral data, design experiments, and build models that ship to production. You'll work closely with engineers to ensure your science translates into low-latency, high-reliability systems. You'll present findings to leadership and influence product strategy. Some weeks you'll be deep in model architecture; other weeks you'll be debugging a relevance gap in production. Every day, your work reaches real customers. About the team We're a small, high-impact team that values scientific rigor and engineering craft equally. The team values innovations and offers a safe place to try, fail and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. Our team offers creative space with entrepreneurial work environment focusing on customer obsession.

Amazon is looking for an Applied Scientist to help build next generation selection/assortment systems. On the Specialized Selection team within the Supply Chain Optimization Technologies (SCOT) organization, we own the selection of the products that Amazon offers in our limited shelf assortment problems world wide. This includes products for our fastest delivery, perishable grocery offerings, and other emerging Amazon delivery programs. The selection is generated with a series of Machine Learning (ML) and optimization models to best cater to customer purchase intents under limited warehouse capacity. We build tools and systems that enable our partners and business owners to scale themselves by leveraging our problem domain expertise, focusing instead on introspecting our outputs and iteratively helping us improve our models rather than hand-managing their assortment. We partner closely with our business stakeholders as we work to develop state-of-the-art, scalable, automated selection management systems. As an Applied Scientist, you will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including supervised and unsupervised machine learning, large language models, mixed integer linear programs (MILPs), reinforcement learning, causal inference, and experiment designs. Some critical research areas in our space include modeling substitutability between similar products, complementarity and basket building, measuring speed sensitivity of products through experiments, optimizing assortment under operational and capacity constraints, and supply and demand forecasting. Key job responsibilities You will be an end-to-end owner for the projects you support. Responsibilities include: Understanding business requirements and existing challenges and map them to the right scientific solution; Designing effective, scalable, and achievable solutions to key business problems; Developing the right set of metrics to evaluate efficacy of your models and solutions; Prototyping and analyzing new models and business logic; Productionizing your scientific solutions, including writing production-quality critical path code; Communicating, both written and verbally, with both technical and business audiences throughout each project; Publishing findings in internal and/or external conferences and interfacing with the scientific community; Mentoring and developing the scientist community across the organization

Are you passionate about solving complex problems and protecting one of the world’s largest cloud platforms? The AWS Payments and Fraud Prevention team is looking for an innovative Applied Scientist to help keep AWS a safe and trusted environment for millions of customers worldwide. In this role, you will design, build, and deploy machine learning models that detect, prevent, and mitigate fraudulent activity across the AWS ecosystem. You will work with massive, real-world datasets, develop new detection strategies, and apply advanced and practical technologies to tackle ever-evolving threats. You will also explore Generative AI (GenAI) techniques to uncover new fraud patterns and strengthen our fraud defenses. At AWS, we support hundreds of thousands of businesses, powering billions of transactions every day. Fraudsters are constantly innovating — and so are we. If you enjoy thinking like a fraudster, building resilient defenses, and making a real-world impact, we invite you to join us and help shape the future of secure cloud computing. Key job responsibilities * Design, build, and deploy machine learning models to detect, prevent, and mitigate fraudulent activities across the AWS platform. * Analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats. * Explore and apply GenAI techniques, including large language models (LLMs), synthetic data generation, and adversarial simulations to enhance fraud detection capabilities. * Collaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions. * Experiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics. * Continuously monitor model performance and improve robustness against adversarial behaviors and evolving fraud tactics. * Communicate findings and technical insights clearly and effectively to both technical and non-technical audiences. * Contribute to the broader fraud prevention strategy, driving innovation and best practices across the organization. About the team AWS Payments and Fraud Prevent is responsible for detecting & mitigating AWS account risks. You’ll be part of a team of Scientists, Analysts, and Technical & non-Technical Program Managers. The team’s goal is to identify and neutralize fraudsters from unauthorized access to legitimate AWS customers accounts. We have a formal mentor search application that lets you find a mentor that works best for you. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members.

The Amazon Robotics team is seeking an experienced Applied Scientist to join our team. In this role you will apply the latest trends in research to solve real-world problems in robotics and AI. You will collaborate with a team of scientists and engineers building these applications. We holistically design, build, and deliver end-to-end robotic systems. Our team is also responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, machine learning scientists, software engineers, and hardware engineers to collaborate and deploy systems in the field. Key job responsibilities • Research, design, develop, and evaluate complex perception, motion planning, and decision making algorithms integrating across multiple disciplines and leveraging machine learning. • Create experiments and prototype implementations of new learning algorithms and prediction techniques. • Work closely with software engineering team members to drive scalable, real-time implementations. • Collaborate with machine learning and robotic controls experts to implement and deploy algorithms, such as machine learning models. • Collaborate closely with hardware engineering team members on developing systems from prototyping to production level. • Represent Amazon in academia community through publications and scientific presentations. • Work with stakeholders across hardware, science, and operations teams to iterate on systems design and implementation. About the team Watch this video to learn more about Vulcan Pick team in Amazon Robotics: https://www.amazon.science/publications/vulcan-pick-a-robotic-system-for-picking-targeted-objects-from-fabric-pods

Amazon’s Middle Mile transportation network runs on physical assets and operations that generate an enormous volume of imagery and video. The Network Engineering, Scheduling, Technology (NEST) Science team within the Amazon Transportation Services organization is looking for an Applied Scientist with deep Computer Vision (CV) expertise and the versatility to apply machine learning broadly, to help turn that visual data into automated, operational decisions, spanning asset condition, automated inspection, object and component detection, and other visual understanding problems across the network. In this role, you will develop, train, and productionize computer vision models across a portfolio of high-impact use cases and business challenges, owning the scientific approach from problem formulation through production. You will work closely with other scientists, business owners, and engineering teams to advance modeling approaches, design rigorous experiments to validate them, and launch them to production. This is a hands-on applied science role with a broad scope and direct, measurable business impact. Key job responsibilities -Develop and apply visual perception and representation learning across image and video domains, including recognition, detection, segmentation, and tracking, with deep spatial and temporal reasoning enabled by modern deep learning and foundation-model architectures. -Own large-scale model training, fine-tuning, and learning from heterogeneous or weakly supervised data, including self-supervised and semi-supervised techniques. -Tackle real-world CV challenges at scale: large unlabeled datasets, class imbalance, high visual variability, and inconsistent capture conditions (angle, lighting, occlusion, motion blur). -Partner with product/program, operations, and engineering stakeholders to translate operational problems into well-defined CV objectives with measurable success criteria. -Design rigorous evaluation frameworks with explicit precision/recall tradeoffs and operating-point selection tied to real-world business cost, including the cost asymmetry between false negatives and false positives. -Build and maintain custom training and inference pipelines, and partner with engineering teams to deploy models into production. -Frame ambiguous operational problems from first principles and select the right approach for each, applying CV where it’s the best tool and other machine learning or simpler methods where they are not.

Employer: Twitch Interactive, Inc. Position: Applied Scientist III - AMZ27946.1 Location: San Francisco, CA Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. (40 hours / week, 8:00am-5:00pm, Salary Range $192200 - $260000) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000

Employer: Amazon.com Services LLC Position: Economist II - AMZ27751.1 Location: Irvine, CA Multiple Positions Available: Work with the chief economist and/or senior management on key business problems faced in retail, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and/or operations. Apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. Build econometric models using data systems. Apply economic theory to solve business problems. Develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems. Apply tools from applied micro-econometrics (e.g. experimental design, difference-in-difference, regression discontinuity, and IV) and forecasting (essential time series models). Leverage big data tools for data extraction. Write up and present analysis for distribution to various levels of management at Amazon. (40 hours / week, 8:00am-5:00pm, Salary Range $136000 - $184000) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000

We are seeking an Applied Science Manager to lead the team that invents how our autonomous mobile robot fleet thinks - from how robots negotiate shared space to adapting fleet behavior as tasks and deadlines change. This is a rare opportunity to own the evolution of multi-robot coordination in free space at an unprecedented scale. Proteus is Amazon's first fully autonomous mobile robot and navigates freely alongside people in our fulfillment centers, perceiving its environment and making decisions in real time. The fleet is growing quickly in scale, diversity, and intelligence, with thousands of robots deployed and new platforms including mobile manipulation entering the mix. The science challenge is making a diverse, heterogeneous fleet perform reliably in a world that changes constantly — where priorities shift hour to hour, new capabilities come online, and operational conditions create new constraints. Your team will own algorithms at multiple layers of this system: how groups of robots autonomously coordinate through intersections and corridors, how fleet traffic is shaped across the road network, which tasks are assigned to which robots and when, and a new fleet orchestration layer that interacts directly with Operations to take in requests, collaborate to solve complex operational challenges, and adapt fleet performance and behavior on the fly. Whether you've been leading a research team in industry and want a bigger canvas, or you've been driving impactful academic research in close partnership with industry and are ready to make the leap, this is a role where your ideas become fleets of robots, and those robots deliver for hundreds of millions of customers. Key job responsibilities • Set the technical vision and research agenda; identify the right problems and sequence bets across near-term production needs and long-horizon research • Build, hire, and develop a team of applied scientists; provide scientific mentorship and grow careers • Drive algorithms from research through production deployment in partnership with engineering • Represent the team externally through publications, academic collaborations, and community engagement A day in the life Your internal stakeholders are engineering teams who productionize your algorithms, operations leaders whose buildings depend on fleet performance, and peer scientists building learned models of fleet dynamics. You spend your time shaping research direction, unblocking technical challenges, reviewing experimental results, and partnering across disciplines to get science into production. About the team You would join a multi-disciplinary science organization with deep expertise in planning, optimization, machine learning, and robotics — working at the frontier of multi-robot coordination where scale, diversity, and dynamism intersect. Our algorithms ship to real robots within months of conception, and we learn from the operational data they generate. We maintain active academic collaborations and contribute to the research community through publications, workshops, and open problems.

Alexa Connections is on a mission to become the world's most trusted communication agent — spanning calls, text, email, and the ever-expanding surfaces where people connect. We're building intelligence that keeps customers connected effortlessly while putting their trust and privacy first. As an Applied Scientist, you'll help build the smartest communications agent for people — an agent that understands intent, context, and relationships, and that acts on a customer's behalf to make every interaction feel effortless and genuinely helpful Key job responsibilities You'll research, develop, and deploy the next generation of on-device AI for communication, bringing small language models, agentic capabilities, and generative intelligence to the edge so that customers get responsive, and private experiences without relying on the cloud. In practice, that means designing on-device arbitration systems that intelligently route between local and cloud inference in real time, applying modern compression techniques like quantization, distillation, and low-rank adaptation to fit frontier-class capabilities into tight memory and latency budgets, and optimizing inference across on-device accelerators such as NPUs and mobile GPUs. You'll build privacy-first personalization that learns and adapts without user data ever leaving the device, and contribute to the science of edge communication intelligence, intent understanding, contact ranking, proactive connectivity, and conversational reasoning, all running locally. Throughout, you'll collaborate closely with hardware, systems, and product teams to co-design models with the target device in mind, and translate research advances into production features that push the state of the art in on-device AI. About the team We're building up the Alexa Connections science team, so you'll join early and grow alongside it. You'll work closely with experienced scientists and engineers invested in your development, with plenty of mentorship, room to broaden your skills, and the chance to own meaningful problems as you grow. It's a supportive place to do impactful work early in your career, shaping how millions of customers connect every day.