Careers

The Amazon Search team creates customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Search Autocomplete and Navigation focuses on helping customers express their shopping intent and navigate search results more effectively. In this role, you will invent universally applicable signals and algorithms to improve suggestion generation, recommendations, and ranking, using LLMs and ML techniques. The improvements you make will help hundreds of millions of customers find the right products faster, from the first keystroke through search result refinement. You will work on problems such as fine-tuning large language models for real-time suggestion generation under strict latency constraints, personalizing recommended content to individual customers, building evaluation frameworks for model selection, and designing data-driven guardrails for LLM-generated content. The work will span the whole development pipeline, including data analysis, evaluation system design, prototyping, A/B testing, and creating production-level systems. Key job responsibilities Your responsibilities include but not limited to: * Analyze the data and metrics resulting from traffic into Amazon's product search service. * Design, build, and deploy effective and innovative ML and LLM solutions to improve search experiences. * Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production. * Publish and present your work at internal and external scientific venues in the fields of ML/NLP/IR.

The Agentic Automated Reasoning Group is building the next generation of software verification tools combining advances in artificial intelligence, the computational capacity of the cloud, and our deep expertise in the domain. Join us if you want to be a part of this transformational endeavor. The Strata team (https://github.com/strata-org) is seeking a Sr. Applied Scientist with broad interest and expertise in interactive theorem proving, programming language semantics, deductive verification and generative AI. You will combine your expertise with that of your coworkers to build new tools that solve code analysis problems previously considered beyond reach. Our application areas span all the way from Infrastructure as Code to high-performance cryptography written in assembly code, while our methods span from interactive theorem proving to automated test generation. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. https://aws.amazon.com/security/provable-security/ Key job responsibilities - End-to-end technical leadership for delivering AR solutions working backwards customer use cases. - Identify tools and methods capable of addressing the verification needs of customers, including any novel analysis capabilities required. - Use tools spanning from fuzzers, property-based testing to model checkers, and interactive theorem provers to establish program properties. - Explore generative AI techniques to help customers formalize their requirements, find revealing tests, generate required boiler plate for testing and model checking, and find and repair program proofs. About the team You will be working with a team of formal verification specialists spanning recently hired PhDs to industry veterans. You will work collaboratively to deliver results in the form of verified code and tools to accelerate code verification for our customer teams.

The Amazon Middle Mile Science team is seeking an Applied Scientist to be part of a team solving complex airline operations problems to reduce cost and improve performance. You will work closely with product, research science and technical leaders throughout Amazon Air, Amazon Delivery Technology and and will be responsible for influencing funding decisions in areas of investment that you identify as critical future product offerings. You will partner with software developers and data scientists to build end-to-end data pipelines and production code, and you will have exposure to senior leadership as we communicate results and provide scientific guidance to the business. You will analyze large amounts of business data, build the or models that will enable us to continually delight our customers worldwide. The ideal candidate will have extensive experience in Science work, business analytics and have the aptitude to incorporate new approaches and methodologies while dealing with ambiguities. Excellent business and communication skills are a must to develop and define key business questions and build models that answer those questions. You should have a demonstrated ability to think strategically and analytically about business, product, and technical challenges. Further, you must have the ability to build and communicate compelling value propositions, and work across the organization to achieve consensus. This role requires a strong passion for customers, a high level of comfort navigating ambiguity, and a keen sense of ownership and drive to deliver results. Key job responsibilities - Partnership with the engineering and operations to drive modeling and design for complex business problems. - Drive full life-cycle projects. - Design and prototype decision support tools (product) to automate standardized processes and optimize trade-offs across the full decision space. - Lead complex modeling analyses to aid management in making key business decisions and set new policies.

We are looking for passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build intelligent, AI-driven solutions that transform how Amazon manages travel and events at scale. As part of the Amazon Travel & Events (AT&E) Program Technology Solutions team, our mission is to provide a seamless and delightful experience for Amazon's business travellers and events programs by raising the bar in Generative AI with Large Language Models (LLMs), Natural Language Understanding (NLU), conversational AI, and Applied Machine Learning (ML). You will work alongside experienced engineers to develop and apply algorithms and modelling techniques that advance the state-of-the-art in conversational AI, intelligent automation, and data-driven decision making. You will gain hands-on experience with Amazon's heterogeneous travel data sources, including contracts, booking systems, supplier data, and event logistics—and large-scale computing resources to accelerate advances in travel and events intelligence at scale. You will also help make it easier for internal customers to use analytics to monitor and model program performance improvements. Key job responsibilities • Design, develop, and evaluate ML models leveraging GenAI, multimodal reasoning, and large-scale information retrieval to solve well-defined catalog understanding challenges such as product identity and relationship inference • Apply and adapt VLMs, foundation models, and LLM-based approaches to address product catalog problems—experimenting with fine-tuning, prompt engineering, and retrieval-augmented generation techniques • Implement model optimization techniques—including distillation, quantization, and serving optimizations—to improve latency, cost, and efficiency of deployed models under guidance from senior scientists • Drive the design and execution of rigorous experiments and ablation studies on large-scale datasets, delivering results with statistical rigor and clear recommendations to the team • Build and iterate on ML pipelines from prototyping through production deployment, writing clean, well-tested, production-quality code • Contribute to improving model reliability by applying uncertainty calibration, confidence estimation, and interpretability techniques to support trustworthy catalog decisions • Collaborate closely with senior scientists, engineers, and product teams to translate business requirements into well-scoped ML solutions • Stay current with the latest research in GenAI, VLMs, and multimodal AI, and identify opportunities to apply new techniques to team problems • Co-author research publications and contribute to internal tech talks and knowledge-sharing initiatives

The Agentic Automated Reasoning Group is building the next generation of software verification tools combining advances in artificial intelligence, the computational capacity of the cloud, and our deep expertise in the domain. Join us if you want to be a part of this transformational endeavor. The Strata team (https://github.com/strata-org) is seeking a Principal Applied Scientist with broad interest and expertise in interactive theorem proving, programming language semantics, deductive verification and generative AI. You will combine your expertise with that of your coworkers to build new tools that solve code analysis problems previously considered beyond reach. Our application areas span all the way from Infrastructure as Code to high-performance cryptography written in assembly code, while our methods span from interactive theorem proving to automated test generation. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. https://aws.amazon.com/security/provable-security/ Key job responsibilities - Define roadmap and lead delivery of AR solutions across multiple customer use cases. - Identify tools and methods capable of addressing the verification needs of customers, including any novel analysis capabilities required. - Use tools spanning from fuzzers, property-based testing to model checkers, and interactive theorem provers to establish program properties. - Explore generative AI techniques to help customers formalize their requirements, find revealing tests, generate required boiler plate for testing and model checking, and find and repair program proofs. About the team You will be working with a team of formal verification specialists spanning recently hired PhDs to industry veterans. You will work collaboratively to deliver results in the form of verified code and tools to accelerate code verification for our customer teams.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. The Data, Insights, Science and Optimization, Music Product and Tech (DISCO MPT) team is looking for a Data Scientist to join a team of scientists and engineers who analyze big data, provide analytics and insights and build models and algorithms to power Music product experiences. In this role, you will set the science vision and direction for the team and collaborate with internal stakeholders across product, science and finance to scale and advance our science offerings. You will lead large scale science solutions, prioritize across multiple stakeholders and projects and be part of a fast-paced, dynamic and fun environment. Key job responsibilities • Lead the research and development of models and science products powering personalized recommendations • Partner with product leaders at Amazon Music to develop science-driven business strategies • Partner with science, marketing and product teams across Amazon Entertainment and subscription businesses • Educate internal teams on analytics, insights and measurement • Develop models to determine drivers of key performance metrics, and automate the process of deep diving into variances • Collaborate with product and engineering teams to evaluate the impact of new features or algorithms (e.g., the Playlist Song Recommendation experiments) • Analyze the results of experiments and provide recommendations to optimize solutions • Partner with Senior Data Scientists and Product Managers to analyze and propose success and guardrail metrics • Encourage the use of experimentation and advanced analytics across the organization About the team The DISCO team focuses on accelerating Amazon Music customer growth by empowering product teams to make sound, customer-centric decisions through data and insights. We build data pipelines, self-service analytics, insights and predictive models enabling acquisition, engagement and retention at scale with personalized customer touchpoints.

Are you passionate about using data science and machine learning to optimize how hundreds of millions of customers experience communications from the world's most customer-centric company? Join the Outbound Communications Intelligence team at Amazon, where you will lead the development of scalable/robust advanced AI based methods like LLMs and RL to personalize the relevance, frequency and timing of messages across push, email, WhatsApp, and SMS channels reaching 250M+ global customers every week. You will lead the insights arm to build highly accurate and world-class self-service analytics solutions that guide the short- and long-term investments for the business. Key job responsibilities You will lead applied scientists, data scientists and business intelligence engineers to: - Optimize Outbound's inbox management and planning system to personalize frequency, send-time and relevance bar of our messages to customers. - Design and execute large-scale experiments such as multi-arm elasticity tests or RCTs to measure and improve incrementality/performance of our models. - Drive development of HVA propensity models (opt-out, purchase, etc.) to drive intended behavior of customers to their next stage of shopping and engagement with Amazon. - Drive AI-based transformation in data accuracy and reporting: migrating and enhancing the self-serve analytics capabilities developed by the team, automating WBR preparation, building anomaly detection, etc. - Own financial planning frameworks for outbound performance including QxG/HVE forecasting and ROI measurement for paid channel investments. In addition, you will: - Hire, develop, and mentor scientists and BIEs while partnering cross-functionally with engineering, product, marketing, and partner science teams (CBA, P13N, CFV) to productionize solutions at scale. - Create, align and evolve your team's roadmap by prioritizing across multiple competing priorities using high judgement decisions.

Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.

As an Applied Scientist in Amazon Fullfilment Technology, you will lead the development of agentic systems to assist with operational decision making and orchestration. You will work building full agentic systems leveraging multi-agent orchestration, tool use, memory, and action execution. You will train LLMs using a combination of rejection sampling approaches, SFT, continual post-training, and Reinforcement Learning (RL). These systems are deployed to Amazon buildings, and you will also work on rigorous offline and online evaluations. Your work will leverage the latest LLMs to develop capabilities for agentic reasoning, coding and analytics. You will also lead research projects to tackle unsolved problems, mentor interns, and author academic papers to summarize your findings for external publication. Key job responsibilities - Generating training and preference data for specific use cases (reasoning trajectories, tool traces) - Reward modeling and policy optimization for LLMs: DPO, IPO, RLHF/RLAIF with PPO/GRPO, rejection sampling. - Supervised fine-tuning on step-by-step trajectories and tool-use traces - Verbal Reinforcement Learning and Continual Learning - RL for LLMs, Offline RL and off-policy evaluation - Agentic memory/state management; episodic and semantic memory; vector search; grounding with RAG. - Evaluation: developing decision quality metrics, scaling LLM-based evaluations. About the team Amazon Fulfillment Technologies (AFT) powers Amazon's global fulfillment network. We invent and deliver software, hardware, and data science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. Learn more about AFT: https://tinyurl.com/AFTOverview

The Amazon Center for Quantum Computing in Pasadena, CA, is looking to hire an Applied Scientist on the Materials team, focused on understanding and mitigating materials-driven loss mechanisms in superconducting quantum processors. You will join a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have deep expertise in materials characterization and computational modeling of disordered solids, with the ability to connect atomic-scale insights to materials design. Candidates with a track record of original scientific contributions in experimental and computational studies of materials defects will be preferred. We are looking for candidates with strong scientific and engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. As an Applied Scientist at CQC, you will be expected to drive materials research from characterization through design recommendations and stay abreast of advances in materials science for superconducting quantum hardware. Key job responsibilities You will combine materials characterization and numerical simulations to investigate how material defects affect qubit performance. This includes implementing multi-technique characterization workflows for thin films and interfaces, providing input on the design of materials with targeted properties, and developing computational tools for simulations of disordered structures. You will provide characterization support for the Fabrication team, investigating materials sources of loss in production-relevant films and processes. You will coordinate with cross-functional teams to translate materials insights into actionable process improvements, and publish results in scientific journals when appropriate.