Careers

Are you a Graduate Student interested in machine learning, natural language processing, computer vision, automated reasoning, robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Key job responsibilities Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. A day in the life Come teach us a few things, and we’ll teach you a few things as we navigate the most customer-centric company on Earth.

As a Principal Applied Scientist for Last Hundred Yards Automation, you will be at the forefront of developing AI and ML solutions that power our autonomous delivery robots. This role combines deep expertise in machine learning, computer vision, and robotics to solve complex challenges in real-world autonomous navigation, obstacle detection, and dynamic path planning. You will work closely with robotics engineers, software developers, and product teams to research, design, and implement sophisticated algorithms that enable safe and efficient last-yard delivery operations. This position requires a unique blend of theoretical knowledge and practical implementation skills, with a focus on transforming research concepts into production-ready solutions that can operate reliably in diverse real-world environments. Key job responsibilities The Principal Applied Scientist will lead the development and implementation of advanced AI/ML models for autonomous navigation, focusing on critical areas such as real-time object detection, semantic scene understanding, and predictive movement planning. You will be responsible for designing and conducting experiments, analyzing complex datasets, and developing novel algorithms to improve robot performance and reliability. The role involves collaborating with cross-functional teams to integrate ML solutions into the robotics platform, optimizing model performance for edge computing, and establishing metrics for continuous improvement. You will also mentor junior scientists, contribute to research publications, and stay current with latest developments in the field. Key activities include developing robust ML pipelines, implementing state-of-the-art deep learning architectures, and creating innovative solutions for challenging problems such as adverse weather navigation, multi-robot coordination, and human-robot interaction. Success in this role requires balancing research excellence with practical implementation, while maintaining a strong focus on safety and reliability in autonomous systems. About the team LMDA (Last Mile Delivery Automation) is a automation system designed to revolutionize the final phase of package delivery, focusing specifically on the last hundred yards from delivery vehicle to customer doorstep. The system integrates autonomous robots equipped with advanced AI/ML algorithms, real-time navigation capabilities, and smart obstacle avoidance technology to efficiently transport packages to their final destination. These robots are designed to navigate various terrains, including sidewalks, building entrances, and residential complexes, while adhering to local safety regulations and maintaining secure delivery protocols. LMDA incorporates cloud-based fleet management, real-time tracking, and dynamic route optimization to ensure seamless coordination between delivery vehicles and robots. The system features a user-friendly interface for both delivery personnel and customers, providing live tracking, delivery notifications, and interactive robot communication. As labor costs rise and delivery volumes increase, LMDA represents a scalable, cost-effective solution that addresses the growing challenges of last-mile delivery while significantly improving operational efficiency and customer satisfaction.

Amazon’s Middle Mile Planning Research and Optimization Science group (mmPROS) is looking for a Senior Applied Scientist specializing in design and evaluation of algorithms for predictive modeling and optimization applied to large-scale transportation planning systems. This includes the development of novel machine learning and artificial intelligence techniques to improve on marketplace optimization solutions. The Middle Mile Science group is charged with developing an evolving suite of optimization tools to facilitate the design of efficient air and ground transport networks, optimize the flow of packages within the network to efficiently align network capacity and shipment demand, set prices, and effectively utilize scarce resources, such as aircraft and trucks. The scale of Amazon’s fulfillment operations challenges us to design, build and operate robust transportation networks that minimize the overall operational cost while meeting all customer deadlines. Real-time execution of our network depends on state-of-the-art artificial intelligence tools to coordinate the actions of thousands of operators and drivers in real time. Amazon often finds existing techniques do not effectively match our unique business needs, which necessitates the innovation and development of new approaches and algorithms to find an adequate solution. As a Sr. Applied Scientist responsible for middle mile transportation, you will be working closely with different teams including business leaders and engineers to design and build scalable products operating across multiple transportation modes. You will create experiments and prototype implementations of new learning algorithms and prediction techniques. You will have exposure to top level leadership to present findings of your research. You will also work closely with other scientists and engineers to implement your models within our production system. You will implement solutions that are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility, and make decisions that affect the way we build and integrate algorithms across our product portfolio. About the team Our Middle Mile Marketplace Science team builds the algorithms for Amazon’s rapidly growing freight marketplace. Amazon contracts with 3P shippers and a network of independent carriers, using a mix of contract structures with varying service and risk profiles. Our work focuses on mechanisms and learning algorithms to optimize pricing and matching in this complex marketplace, and continually improve the experience for carriers and shippers. This is an area with many challenging problems and a huge business impact for Amazon!

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 ensure uniqueness and consistency of product identity and 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 are seeking an experienced Senior Applied Scientist to join our camera technology team. This role will be instrumental in defining and implementing the next generation of imaging technologies for Amazon's devices. Key job responsibilities ISP Architecture design: The architect leads gathers requirements from stakeholders, product managers, internal and external vendors (sensors and SoC) as well as the customers (image quality and CV), and builds design specifications for camera ISP architecture. That includes functional blocks, data flow, use cases, management of system resources (compute, memory bandwidth, PPA), pipeline design, prototyping and performing feasibility studies as needed. Algorithm Design & Implementation: Design novel and improve existing algorithms for implementation in ASIC or software, classical/ML/DL/AI, requiring deep understanding of science foundations and the state-of-the-art, system tradeoffs, and attention to detail. Cross-Functional Collaboration: Working with cross-functional teams to solve imaging problems and invent to deliver still and video experiences using traditional computational imaging and machine learning, both on-device and in the cloud. Vendor Management: Communicating with vendors to clarify design nuances and understand limitations, and overseeing image tuning to ensure the final pipeline meets customer expectations. Image Quality Leadership: Combining deep knowledge of imaging science with passion for beautiful images, applying image quality tuning principles, testing methodologies, and subjective assessment. Inventive Problem Solving: Applying proven problem-solving and research skills to address imaging challenges across various Amazon camera products. About the team Amazon Lab126 is an inventive research and development organization that designs and engineers high-profile consumer electronics. We are the team behind Amazon Echo devices, Fire tablets, Home robot, Fire TV, and Cloud Cam.

Amazon has launched a new research lab in San Francisco to develop foundational capabilities for useful AI agents. We’re enabling practical AI to make our customers more productive, empowered, and fulfilled. In particular, our work combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments. Our research builds on that of Amazon’s broader AGI organization, which recently introduced Amazon Nova, a new generation of state-of-the-art foundation models (FMs). Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. We’d love for you to join our lab and build it from the ground up! Key job responsibilities You will be responsible for maintaining our task management system which supports many internal and external stakeholders and ensures we are able to continue adding orders of magnitude more data and reliability.

Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. We are seeking a talented Applied Scientist to join our advanced robotics team, focusing on developing and applying cutting-edge simulation methodologies for advanced robotics systems. This role centers on research and development of physics-based simulation techniques, sim-to-real transfer methods, and machine learning approaches that enable rapid development, testing, and validation of robotic systems operating in complex, real-world environments. Key job responsibilities - Advance physics-based simulation fidelity for contact-rich manipulation and locomotion - Design and build high-performance simulation tools integrated into a production robotics stack - Translate research ideas into robust, scalable software pipelines - Develop methods to quantify and reduce simulation-to-reality gaps across design, safety, and control - Architect scalable simulation solutions for rigid and deformable body dynamics - Build simulation pipelines optimized for large-scale reinforcement and policy learning - Establish frameworks for continuous simulation improvement using real-world deployment data - Collaborate with engineering, science, and safety teams on simulation requirements and validation About the team Our team is building a comprehensive simulation platform for advanced robotics development, combining locomotion and manipulation capabilities. We operate at the cutting edge of physics simulation, reinforcement learning, and sim-to-real transfer, collaborating with world-class robotics engineers, applied scientists, and mechanical designers in a fast-paced, innovation-driven environment. This role uniquely combines fundamental research with real-world deployment. You will pursue core research questions in physics-based simulation while seeing your work translated into production systems, validated on real hardware, and informed by deployment data. Working alongside Simulation Software Engineers, you will help transform research ideas into scalable, production-grade simulation capabilities that directly impact how robots are designed, trained, and deployed.

Selection Monitoring team is responsible for making the biggest catalog on the planet even bigger. In order to drive expansion of the Amazon catalog, we develop advanced ML/AI technologies to process billions of products and algorithmically find products not already sold on Amazon. We work with structured, semi-structured and Visually Rich Documents using deep learning, NLP and image processing. The role demands a high-performing and flexible candidate who can take responsibility for success of the system and drive solutions from research, prototype, design, coding and deployment. We are looking for Applied Scientists to tackle challenging problems in the areas of Information Extraction, Efficient crawling at internet scale, developing models for website comprehension and agents to take multi-step decisions. You should have depth and breadth of knowledge in text mining, information extraction from Visually Rich Documents, semi structured data (HTML) and advanced machine learning. You should also have programming and design skills to manipulate Semi-Structured and unstructured data and systems that work at internet scale. You will encounter many challenges, including: - Scale (build models to handle billions of pages), - Accuracy (requirements for precision and recall) - Speed (generate predictions for millions of new or changed pages with low latency) - Diversity (models need to work across different languages, market places and data sources) You will help us to - Build a scalable system which can algorithmically extract information information from world wide web. - Intelligently cluster web pages, segment and classify regions, extract relevant information and structure the data available on semi-structured web. - Build systems that will use existing Knowledge Base to perform open information extraction at scale from visually rich documents. Key job responsibilities: - Use AI, NLP and advances in LLMs/SLMs and agentic systems to create scalable solutions for business problems. - Efficiently Crawl web, Automate extraction of relevant information from large amounts of Visually Rich Documents and optimize key processes. - Design, develop, evaluate and deploy, innovative and highly scalable ML models. - Work closely with software engineering teams to drive real-time model implementations. - Establish scalable, efficient, automated processes for large scale model development, model validation and model maintenance. - Lead projects and mentor other scientists, engineers in the use of ML techniques. - Publish innovation in research forums. Basic qualifications: - 3+ years experience building ML models for business application. - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience. - Experience programming in Python, Java, C++ or related languages. - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing. Preferred qualifications: - Experience using Unix/Linux. - Experience in professional software development. - Experience in patents or publications at top-tier peer-reviewed conferences or journals. - Master's degree or above in computer science, computer engineering, or related field.

The AWS Marketplace & Partner Services (AMPS) Science team is at the forefront of driving AWS's AI-powered growth through developing and evaluating the next generation search, recommendations, and agentic systems. As a Principal Applied Scientist on our team, you'll own the technical strategy and execution for our most ambitious AI-driven initiatives that directly accelerate AWS revenue growth through both customer discovery experiences and partner co-selling capabilities. Your work will establish Marketplace as customers' primary solution discovery and transaction platform while simultaneously transforming how AWS partners scale their business through insights and agentic workflow automation. You will be a trusted technical leader who tackles intrinsically complex scientific problems by leveraging deep expertise in information retrieval, recommendation systems, LLMs, and agentic systems. You'll innovate and set the standard for scientific excellence, making decisions that affect how products are built and integrated across AMPS. You'll align teams toward coherent strategies, guide peers in adopting the latest scientific trends, and force-multiply by decomposing hard problems and executing through collaboration. Key job responsibilities - Define direction for next-generation recommendation systems, including deep personalization, agentic architecture, and primitives that integrate customer usage patterns, infrastructure requirements, and business objectives to deliver personalized, outcome-oriented guidance - Lead information retrieval innovations, multi-objective ranking, and reasoning over heterogeneous solution types - Architect, implement, and design agentic AI systems that orchestrate complex workflows - Bridge theoretical innovations with practical solutions, making critical judgments to select the best technical approaches for both short and long-term objectives - Mentor and guide applied scientists, holding the team to high standards of technical rigor and scientific excellence - Contribute to the broader scientific community through patents, publications at conferences, and engagement with academic partners About the team The AWS Marketplace & Partner Services Science team is at the forefront of developing and deploying AI/ML systems that serve multiple critical stakeholders: - AWS Customers: Through the AWS Marketplace, we support discovery experiences that streamline cloud adoption and innovation. - AWS Partners: Via Partner Central, we offer advanced tools and insights to enhance collaboration and drive mutual growth. - Internal AWS Sellers: We equip our sales teams with data-driven recommendations to better serve our customers and partners.

You will be working with a unique and gifted team developing exciting products for consumers. The team is a multidisciplinary group of engineers and scientists engaged in a fast paced mission to deliver new products. The team faces a challenging task of balancing cost, schedule, and performance requirements. You should be comfortable collaborating in a fast-paced and often uncertain environment, and contributing to innovative solutions, while demonstrating leadership, technical competence, and meticulousness. Your deliverables will include development of thermal solutions, concept design, feature development, product architecture and system validation through to manufacturing release. You will support creative developments through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques. Key job responsibilities In this role, you will: - Own thermal design for consumer electronics products at the system level, proposing thermal architecture and aligning with functional leads - Perform CFD simulations using tools such as Star-CCM+ or FloEFD to assess thermal feasibility, identify risks, and propose mitigation options - Generate data processing, statistical analysis, and test automation scripts to improve data consistency, insight quality, and team efficiency - Plan and execute thermal validation activities for devices and SoC packages, including test setup definition, data review, and issue tracking - Work closely with cross-functional and cross-geo teams to support product decisions, generate thermal specifications, and align on thermal requirements - Prepare clear summaries and reports on thermal results, risks, and observations for review by cross-functional leads About the team Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced innovative devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create?