Careers

Unleash Your Potential at the Forefront of AI Innovation At Amazon, we're on a mission to revolutionize the way the world leverages machine learning. Amazon is seeking graduate student scientists who can turn revolutionary theory into awe-inspiring reality. As an Applied Science Intern focused on Information and Knowledge Management in Machine Learning, you will play a critical role in developing the systems and frameworks that power Amazon's machine learning capabilities. You'll be at the epicenter of this transformation, shaping the systems and frameworks that power our cutting-edge AI capabilities. Imagine a role where you develop intuitive tools and workflows that empower machine learning teams to discover, reuse, and build upon existing models and datasets, accelerating innovation across the company. You'll leverage natural language processing and information retrieval techniques to unlock insights from vast repositories of unstructured data, fueling the next generation of AI applications. 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 Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara, CA; Seattle, WA. Key job responsibilities We are particularly interested in candidates with expertise in: Knowledge Graphs and Extraction, Neural Networks/GNNs, Data Structures and Algorithms, Time Series, Machine Learning, Natural Language Processing, Deep Learning, Large Language Models, Graph Modeling, Knowledge Graphs and Extraction, Programming/Scripting Languages In this role, you'll collaborate with brilliant minds to develop innovative frameworks and tools that streamline the lifecycle of machine learning assets, from data to deployed models in areas at the intersection of Knowledge Management within Machine Learning. You will conduct groundbreaking research into emerging best practices and innovations in the field of ML operations, knowledge engineering, and information management, proposing novel approaches that could further enhance Amazon's machine learning capabilities. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. 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. A day in the life - Develop scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Design, development and evaluation of highly innovative ML models for solving complex business problems. - Research and apply the latest ML techniques and best practices from both academia and industry. - Think about customers and how to improve the customer delivery experience. - Use and analytical techniques to create scalable solutions for business problems.

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. 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. 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.

Unlock the Future with Amazon Science! Calling all visionary minds passionate about the transformative power of machine learning! Amazon is seeking boundary-pushing graduate student scientists who can turn revolutionary theory into awe-inspiring reality. Join our team of visionary scientists and embark on a journey to revolutionize the field by harnessing the power of cutting-edge techniques in bayesian optimization, time series, multi-armed bandits and more. At Amazon, we don't just talk about innovation – we live and breathe it. You'll conducting research into the theory and application of deep reinforcement learning. You will work on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry. You will propose and deploy solutions that will likely draw from a range of scientific areas such as supervised, semi-supervised and unsupervised learning, reinforcement learning, advanced statistical modeling, and graph models. 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 Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara, CA; Seattle, WA. Key job responsibilities We are particularly interested in candidates with expertise in: Optimization, Programming/Scripting Languages, Statistics, Reinforcement Learning, Causal Inference, Large Language Models, Time Series, Graph Modeling, Supervised/Unsupervised Learning, Deep Learning, Predictive Modeling 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 Reinforcement Learning and Optimization within Machine Learning. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on developing novel RL algorithms and applying them to complex, real-world challenges. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. 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. A day in the life - Develop scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Design, development and evaluation of highly innovative ML models for solving complex business problems. - Research and apply the latest ML techniques and best practices from both academia and industry. - Think about customers and how to improve the customer delivery experience. - Use and analytical techniques to create scalable solutions for business problems.

The Artificial General Intelligence (AGI) Customization Team is seeking a highly skilled and experienced Applied Scientist to support adoption and enable customization of Amazon Nova. The role focuses on developing state-of-the-art services and tools for model customization, including supervised fine-tuning, reinforcement learning, and knowledge distillation across large language models. As an Applied Scientist, you will play a important role in developing advanced customization capabilities that enable enterprises to build highly performant application-specific models without the need for training models from scratch. Your work will directly impact how companies leverage Amazon Nova models for their specific use cases. Key job responsibilities - Contribute to the development of novel customization techniques including extended post-training, continued pre-training, and advanced knowledge distillation - Collaborate with cross-functional teams to design and implement enterprise-ready tooling for various training techniques on Amazon SageMaker - Design and execute experiments to optimize model accuracy, latency, and cost across different customization approaches (SFT, DPO, PPO) - Develop and enhance preference learning algorithms and training curricula for customer-specific applications - Create robust evaluation frameworks for assessing model performance across different domains and use cases - Contribute to the development of the Responsible AI toolkit, including creating training and evaluation datasets for model alignment - Design and implement secure access mechanisms for early model checkpoints and weights - Communicate technical insights and results to both technical and non-technical stakeholders through presentations and documentation

Interested to build the next generation Financial systems that can handle billions of dollars in transactions? Interested to build highly scalable next generation systems that could utilize Amazon Cloud? Massive data volume + complex business rules in a highly distributed and service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide. Key job responsibilities • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. A day in the life • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. About the team The FinAuto TFAW(theft, fraud, abuse, waste) team is part of FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conduct research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinate with Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers

Are you a PhD student interested in machine learning, natural language processing, computer vision, automated reasoning, or 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 Science Intern, 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. 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. Our scientists use our working backwards method to enrich the way we live and work. To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. For more information on the Amazon Science community please visit https://www.amazon.science.

The Private Brands Discovery team designs innovative machine learning solutions to drive customer awareness for Amazon’s own brands and help customers discover products they love. Private Brands Discovery is an interdisciplinary team of Scientists and Engineers, who incubate and build disruptive solutions using cutting-edge technology to solve some of the toughest science problems at Amazon. To this end, the team employs methods from Natural Language Processing, Deep learning, multi-armed bandits and reinforcement learning, Bayesian Optimization, causal and statistical inference, and econometrics to drive discovery across the customer journey. Our solutions are crucial for the success of Amazon’s own brands and serve as a beacon for discovery solutions across Amazon. This is a high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and engineers. As a scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.. With a focus on bias for action, this individual will be able to work equally well with Science, Engineering, Economics and business teams. Key job responsibilities Key job responsibilities - Experience in causal ML and treatment effect estimation, including methods like propensity scoring, doubly robust estimators, and uplift modeling. - Strong background in Python, ML pipelines, and deploying models to production with robust monitoring and evaluation. - Familiarity with causal inference frameworks and translating business questions into actionable causal insights. - Drive applied science projects in machine learning end-to-end: from ideation over prototyping to launch. For example, starting from deep scientific thinking about new ways to support customers’ journeys through discovery, you analyze how customers discover, review and purchase Private Brands to innovate marketing and merchandising strategies. - Propose viable ideas to advance models and algorithms, with supporting argument, experiment, and eventually preliminary results. - Invent ways to overcome technical limitations and enable new forms of analyses to drive key technical and business decisions. - Present results, reports, and data insights to both technical and business leadership. - Constructively critique peer research and mentor junior scientists and engineers. - Innovate and contribute to Amazon’s science community and external research communities.

Are you passionate about using data science to transform how businesses understand and optimize customer interactions at scale? Do you want to build the models and analytics that power the next generation of AI-driven customer experiences while working directly with customers to accelerate production deployments? As a Senior Applied Scientist within the Applied AI Solutions team, you will collaborate across AI Velocity Teams (AIVT), enabling multiple customer engagements simultaneously. You will lead data science initiatives that span the full lifecycle — from identifying high-value business problems and formulating hypotheses, through rigorous experimentation and modeling, to deploying production-grade solutions that serve thousands of customers. You will bring deep expertise in statistical inference, machine learning, and experimental design to drive measurable impact across Amazon Connect's analytics products and broader Connect AI initiatives. A critical dimension of this role is working directly with customers during production pilots to accelerate time-to-value. You will partner with Applied AI Solutions Architects and Customer Success Specialists to design, build, and deploy AI solutions in customer environments during fixed deployment cycles. You will enable field teams with data-driven insights, reusable analytical assets, ROI tools, and scalable tooling that accelerate customer engagements and solution delivery. Your work will directly influence customer decisions to adopt Connect Customer AI by quantifying business outcomes and demonstrating measurable value. You will operate with significant autonomy, owning the scientific direction of your projects while collaborating with applied scientists, software engineers, product managers, technical, and business stakeholders. You will be expected to identify the right methodology for each problem — whether that's a classical statistical approach, a modern deep learning technique, or a novel combination — and communicate your findings clearly to both technical and non-technical audiences. This role spans Connect AI initiatives including conversational analytics and agentic AI capabilities, offering the opportunity to pioneer data science approaches that scale intelligent analytics worldwide. Key job responsibilities - Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements and business decisions - Work directly with customers during production pilots to design, build, and deploy AI solutions that demonstrate measurable business value - Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes on customer outcomes - Build ROI models and business case tools that quantify the value of Connect Customer AI for existing customers transitioning from Connect Customer Basic - Develop and maintain forecasting systems for demand prediction, capacity planning, and workforce optimization - Develop and apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale - Partner with applied scientists and software engineers to productionize models, ensuring reliability, monitoring, and operational excellence - Enable AI Velocity teams with reusable analytical assets, diagnostic notebooks, and scalable tooling that accelerate customer engagements - Build benchmarking studies and optimization frameworks that demonstrate value across customer cohorts - Own success metrics and create mechanisms to measure model performance, adoption, and business impact - Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations - Operate as a shared resource across 2-3 AIVT teams simultaneously, providing data science expertise across multiple customer engagements A day in the life - Start the morning on a call with the AI Velocity Teams preparing for a strategic customer engagement — reviewing the analytical assets and dashboards you've built, walking through how to interpret model outputs, and tailoring recommendations to the customer's contact center environment - Join a customer working session where you're deploying a production pilot — analyzing their historical contact data, building demand forecasting models, and demonstrating how AI optimizations will reduce their cost per serviced contact while improving customer experience metrics - Dive into a deep analysis triggered by AIVT field feedback — a large enterprise customer is seeing unexpected patterns in their contact data, and you're pulling together multi-source data to isolate root cause and build a reusable diagnostic notebook the AIVT team can leverage for similar cases - Participate in a Conversational Analtyics science review, presenting your A/B test results on a new sentiment classification approach and discussing trade-offs between model accuracy and inference latency with the engineering team - Spend the afternoon building a reusable ROI calculator that field teams can use across customer engagements — packaging your economic models with configurable parameters so teams can quickly quantify the value of Connect Customer AI for different customer profiles and usage patterns - Collaborate with AI Architects and Customer Success Specialists across your three active AIVT engagements, providing data science guidance on model selection, evaluation frameworks, and success metrics for each customer's unique use cases - Wrap up by reviewing a design document for an agentic AI feature that will use conversation analytics to automatically surface coaching recommendations for contact center supervisors, providing feedback on the evaluation methodology and success metrics About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & 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, mentorship 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 we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

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