Careers

Shape the Future of Human-Machine Interaction Are you a master of natural language processing, eager to push the boundaries of conversational AI? Amazon is seeking exceptional graduate students to join our cutting-edge research team, where they will have the opportunity to explore and push the boundaries of natural language processing (NLP), natural language understanding (NLU), and speech recognition technologies. Imagine waking up each morning, fueled by the excitement of tackling complex research problems that have the potential to reshape the world. You'll dive into production-scale data, exploring innovative approaches to natural language understanding, large language models, reinforcement learning with human feedback, conversational AI, and multimodal learning. Your days will be filled with brainstorming sessions, coding sprints, and lively discussions with brilliant minds from diverse backgrounds. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated.. Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for Natural Language Processing & Speech Applied Science Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA. Key job responsibilities We are particularly interested in candidates with expertise in: NLP/NLU, LLMs, Reinforcement Learning, Human Feedback/HITL, Deep Learning, Speech Recognition, Conversational AI, Natural Language Modeling, Multimodal Learning. In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems and to communicate research findings clearly. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. Critically review and validate outputs from AI tools and automated systems. A day in the life - Develop novel, scalable algorithms and modeling techniques that advance the state-of-the-art in natural language processing, speech recognition, text-to-speech, question answering, and conversational modeling. - Tackle groundbreaking research problems on production-scale data, leveraging techniques such as LSTM, transformer-based models, signal processing, information extraction, audio processing, speaker detection, large language models, and multilingual modeling. - Collaborate with cross-functional teams to solve complex business problems, leveraging your expertise in NLP/NLU, LLMs, reinforcement learning, human feedback/HITL, deep learning, speech recognition, conversational AI, natural language modeling, and multimodal learning. - Thrive in a fast-paced, ever-changing environment, embracing ambiguity and demonstrating strong attention to detail.

Revolutionize the Future of AI at the Frontier of Applied Science Are you a brilliant mind seeking to push the boundaries of what's possible with artificial intelligence? Join our elite team of researchers and engineers at the forefront of applied science, where we're harnessing the latest advancements in natural language processing, deep learning, and generative AI to reshape industries and unlock new realms of innovation. As an Applied Science Intern, you'll have the unique opportunity to work alongside world-renowned experts, gaining invaluable hands-on experience with cutting-edge technologies such as large language models, transformers, and neural networks. You'll dive deep into complex challenges, fine-tuning state-of-the-art models, developing novel algorithms for named entity recognition, and exploring the vast potential of generative AI. This internship is not just about executing tasks – it's about being a driving force behind groundbreaking discoveries. You'll collaborate with cross-functional teams, leveraging your expertise in statistics, recommender systems, and question answering to tackle real-world problems and deliver impactful solutions. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for LLM & GenAI Applied Science Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA; Pittsburgh, PA. Key job responsibilities We are particularly interested in candidates with expertise in: LLMs, NLP/NLU, Gen AI, Transformers, Fine-Tuning, Recommendation Systems, Deep Learning, NER, Statistics, Neural Networks, Question Answering. In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of LLMs and GenAI. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on recommendation systems, question answering, deep learning and generative AI. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems and to communicate research findings clearly. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. Critically review and validate outputs from AI tools and automated systems. A day in the life - Collaborate with cross-functional teams to tackle complex challenges in natural language processing, computer vision, and generative AI. - Fine-tune state-of-the-art models and develop novel algorithms to push the boundaries of what's possible. - Explore the vast potential of generative AI and its applications across industries. - Attend cutting-edge research seminars and engage in thought-provoking discussions with industry luminaries. - Leverage state-of-the-art computing infrastructure and access to the latest research papers to fuel your innovation. - Present your groundbreaking work and insights to the team, fostering a culture of knowledge-sharing and continuous learning.

Do you want to join an innovative team applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about working with large-scale datasets and developing models that solve real-world fraud and risk challenges? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to help develop scalable machine learning solutions that safeguard millions of transactions every day. In this role, you will partner with senior scientists and engineers to translate business problems into data-driven solutions, build and evaluate models, and contribute to next-generation risk prevention systems, including applications of Generative AI and LLM technologies. Key job responsibilities Apply machine learning and statistical techniques to build and improve risk management models Analyze large-scale historical data to identify risk patterns and emerging trends Develop, validate, and deploy innovative models under the guidance of senior scientists Experiment with emerging technologies, including GenAI/LLMs, to enhance automation and risk evaluation Collaborate closely with software engineers to implement models in real-time production systems Partner with operations and business teams to improve risk policies and operational efficiency Build scalable, automated pipelines for data analysis, model training, and validation Monitor model performance and provide clear reporting on key risk and business metrics Research and prototype new modeling approaches to improve system performance

Join our Amazon Private Brands Selection Guidance organization in building science and tech solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon. The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon. As a Data Scientist you will investigate business problems using data, invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and prototypes. This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact. Key job responsibilities Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions. Perform data analysis and build data pipelines to drive business decisions. Invent novel science solutions, develop prototypes, and deploy production software to solve business problems. Review and guide science solutions across the team. Publish and socialize your and the team's research across Amazon and external avenues as appropriate Leverage industry best practices to establish repeatable applied science practices, principles & processes.

We are seeking a Human-Robot Interaction (HRI) Research Scientist to develop cutting-edge interactions that make robots feel alive, personal, and fun. In this role, you will focus on verbal and non-verbal conversational systems, social dynamics, memory, and long-term relationship formation between robots, their environments, and the people they interact with. Your contributions will be essential in advancing robotics by enabling expressive, socially intelligent, and trustworthy interactions between robots and humans.

We are looking for an Applied Scientist II to build the AI behind Auto Optimization: agentic systems that automatically optimize advertisers' campaigns on their behalf. Guided by an advertiser's standing instructions, these agents observe how a campaign is performing, reason about what to change, and act on it continuously as conditions in the marketplace shift. Optimizing a campaign well is a collection of decisions. It means working across every control an advertiser has at once: the keywords and products they target, the bids they set, the budgets they allocate, and where their ads appear, all aligned with the preferences of the advertiser. These choices are connected, since a change to targeting changes the right bid, and a change in bids changes how budget should be spent. You will build agents that make these decisions together rather than one lever at a time, and that adapt across many different campaign types and advertiser goals, from growing sales on established products to reaching new customers and launching new ones. Working backwards from the needs of millions of advertisers, you will solve ambiguous problems, invent new methods, and deliver them into a live product that manages real campaigns. You will stay deeply hands-on with the hardest technical problems, collaborate with product and engineering partners on approach, and help raise the quality of the team's science. Key job responsibilities - Build agentic systems that automatically optimize campaigns on an advertiser's behalf, working holistically across every control they have (targeting, bids, budgets, and placements) rather than one lever at a time, and generalizing across many campaign types and advertiser goals, from scaling a proven product to reaching new customers and launching something new. - Encode the dynamics of the auction and marketplace into how the agent reasons, balancing advertiser return, shopper experience, and marketplace health. - Turn raw signal into intelligence by defining and curating the datasets that train and evaluate these agents, from campaign and marketplace data to auction and bid/budget signals, impressions, clicks, conversions, and search-term performance. - Push the frontier of agent design, building the core of the agent itself: planning, tool use (for example, auction simulation, ML models, and optimization routines), and long-horizon reasoning across decisions that interact, and writing the production-quality, critical-path code that carries it from prototype to launch. - Set the bar for trust by developing the evaluation and safety methods that make it trustworthy to let an agent act on live campaigns and real budgets. - Grow with a team that grows the field: contribute to our scientific agenda, learn alongside strong scientists and engineers, and share your work with the broader community. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. This team within Sponsored Products and Brands is focused on guiding and supporting millions of advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon's retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as autonomously managing campaigns based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities, from high-level product guidance down to granular keyword recommendations, and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.

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

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

We are seeking an Applied Scientist to contribute to the research and development of novel security validation and monitoring techniques for AI systems at scale. You will own and contribute to four critical work-streams: 1. Real-Time Agent Monitoring Design and implement scientific approaches for continuous behavioral analysis of AI agents in production—detecting anomalous actions, prompt injection exploitation, and policy violations in real time. 2. Protection & Automated Remediation Invent and deliver novel protection technologies and automated remediation techniques, building on research in security, cryptography, privacy, automated reasoning, and other domains, to enable safe and secure agentic AI models and AI applications. 3. AI Application and Capabilities Validation Invent and deliver scalable methodologies for security testing of AI applications and AI capabilities (e.g. MCP, skills), including adversarial robustness evaluation, safety guardrail bypass detection, tool-use authorization boundaries, and trust boundary verification. 4. AI Asset Discovery & Inventory Research and build scalable techniques to discover, identify, and catalog automatically all AI-enabled applications, services, and capabilities across the company—maintaining a comprehensive, continuously updated database of AI assets. Key job responsibilities Key job responsibilities Invent • Identify and frame new research challenges in AI security where problems are ill-defined and require novel scientific paradigms at the product level. • Contribute to the team's scientific agenda for agent monitoring, protection, remediation, validation research, and AI asset discovery. • Publish research results at peer-reviewed internal and external venues (e.g., USENIX Security, ACM CCS, IEEE S&P, NeurIPS, ICML security workshops, ICSE, PETS) when appropriate. • Articulate key scientific challenges of current and future AI security threats and deliver novel research to address them. • Design, implementation, and successful delivery of scientifically complex security solutions into production—both brand new systems and evolutions of existing ones. • Write significant portions of critical-path code for detection models, validation engines, protection technologies, and asset discovery / classification systems. • Assess and select appropriate technologies (e.g., data protection, private inference, graph-based anomaly detection, NLP-based service classification, code/traffic analysis for AI fingerprinting) for production systems. • Use best practices in scientific methodology and software engineering across the team; provide insightful peer reviews of code, design, and architecture artifacts. • Deliver solutions that are inventive, maintainable, scalable, and extensible. Influence • Autonomously drive discussions with security engineers, software engineers, product managers, and scientist peers across multiple teams. • Build consensus on larger cross-team security initiatives and factor complex efforts into independent workstreams. • Identify and resolve endemic problems, including areas where current security tooling limits innovation of partner teams. • Contribute to the broader internal and external scientific communities as a subject matter expert in AI security. About the team Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

The Foundational AI (FAI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs). Key job responsibilities As an Applied Scientist with the FAI team, you will support the development of RL Gyms, assess their usefulness for the frontier model advancement and build techniques to advance the state of the art with LLMs. You will support the foundational model development in an applied research role. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. A day in the life You start your morning reviewing experimental results from a model training run you kicked off the previous afternoon, analyzing metrics and refining your approach. By mid-morning, you are whiteboarding a new algorithmic design with engineering partners, working through system integration details. After lunch, you lead a science review where you present findings and gather feedback from peers. Later, you pair with a junior scientist to debug a data pipeline issue, turning it into a coaching moment. Your day wraps up drafting a section of a research paper capturing your team's latest results. About the team The FAI team has a mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.