Careers

Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, mobile manipulators, and future platforms with dynamic stability. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin robots operating alongside people at Amazon's unprecedented scale We are seeking a Postdoctoral Scholar to join the SAF Lab. In this role, you will perform research around safe autonomy on highly dynamic robots, with a special focus on loco-manipulation and dynamically stable robots. This includes, but is not limited to, underlying theory of control barrier functions (CBFs) that enables robust and performant safety on hardware, safe reinforcement learning for agile and robust whole-body control, layered safety filters that interface with learning modules, and the synthesis of CBFs from perception data and semantic information. You will push the boundaries of safe autonomy and validate your discoveries experimentally on the next generation of robotic platforms. The SAF lab provides a unique opportunity to collaborate with the inventor of CBFs, top scientists and engineers at Amazon developing the next generation of safe autonomy, while also establishing strong connections with top academic research labs. Your research in the SAF lab will lay the foundations of safe learning on complex robots – removing bottlenecks to deployment and enable them to safely operate around humans. Key job responsibilities In this role you will: • Push forward the fundamental science of safe autonomy. This can be from a variety of perspectives: theoretic contributions, integration with learning, or synthesis from perception. Especially valuable are methods that bridge these different domains. • Develop the simulation and evaluation pipelines needed to run complex and large-scale validation of methods developed in high fidelity simulation environments. • Develop sim-to-real transfer pipelines that enable the deployment of simulation-based methods (controllers, policies) on hardware. • Deploy the methods developed on hardware, with a focus on dynamically stable robots. Validate the underlying science developed in practice and identify gaps between the science and practice to drive innovation in research. • Publish research at top-tier robotics, control and ML venues and contribute to Amazon's scientific reputation in advanced robotics • Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots). A day in the life 0

We are seeking an Applied Scientist to help build Amazon’s next-generation customer memory and personalization systems. Are you interested in building systems that move beyond reacting to customer behavior, to actually understanding and remembering it over time? Our team is building Amazon’s customer memory layer – a system that extracts, curates, and reasons over customer knowledge to power next-generation personalization. This includes transforming noisy, unstructured signals into durable, high-quality representations of customer preferences, intents, and life events, and using them in real time to improve customer experiences. We are part of Amazon’s Personalization organization, a high-performing group that leverages large-scale machine learning, generative AI, and distributed systems to deliver highly relevant customer experiences. We tackle challenging problems at the intersection of information extraction, knowledge representation, LLM reasoning, and recommendation systems. Our systems operate under real-world constraints of scale, latency, and quality, requiring careful tradeoffs between precision, recall, and responsiveness. This team plays a central role in defining how Amazon understands its customers, and how that understanding is applied across the shopping experience. As an Applied Scientist, you will design and build ML and LLM-powered solutions for Amazon's customer memory and personalization systems. You will work on how customer knowledge is extracted, validated, and applied in production systems. You will own the end-to-end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation, and production deployment at scale. You will deliver high-quality, scalable systems that power customer-facing experiences. You will drive work across areas such as fact extraction, memory quality and lifecycle, temporal reasoning, and grounded personalization, while navigating tradeoffs between quality, latency, and coverage. You will collaborate closely with engineering and product teams to translate research into measurable customer impact. Please visit https://www.amazon.science for more information.

Come work with other scientists and software developers in Alexa Smart Home to design and build the next generation of Smart Home experiences using the latest multimodal Large Language Models (LLM's) and Computer Vision. We are evolving Alexa into an intelligent, indispensable companion that automates daily routines, simplifies interaction with appliances, electronics, and cameras and alerts when something unusual is detected. We are focused on making Alexa the user interface for the home, from the simplest voice commands (turn on the lights, turn down the heat) to use cases spanning home security, home entertainment, and the home environment. You can be part of a team delivering features that are highly anticipated by media and well received by our customers. And, you will have the satisfaction of working on a product your friends and family can relate to, and want to use every day. As a Senior Applied Scientist, you will work with other scientists and software developers to design and build the next generation of Smart Home control using the latest Large Language Models and Vision Language Models. And, you will have the satisfaction of working on a product your friends and family can relate to, and want to use every day. Key job responsibilities - Develop new inference and training techniques to improve the performance of LLM's for Smart Home control and Automation - Solve hard problems in computer vision, video summarization, and video search - Develop robust techniques for synthetic data generation for training large models and maintaining model generalization - Mentoring junior scientists to improve their skills, knowledge, and their ability to get things done About the team We are a team of Scientists, Machine Learning Engineers, and Software Developers that work together to make Alexa more insightful and proactive through ambient intelligence - with features like Alexa Hunches that automatically control Smart Home devices. We are interdisciplinary and we act like it -- we ask each other questions and value our different perspectives.

Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team! Key job responsibilities * Design, develop, and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision Models. * Use SQL to query and analyze the data. * Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. * Use machine learning and analytical techniques to create scalable solutions for business problems. * Research and implement novel machine learning and statistical approaches. * Mentor interns. * Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. About the team When a customer returns a package to Amazon, the request and package will be passed through our WWRR machine learning (ML) systems so that we could improve the customer experience, identify return root cause, optimize re-use, and evaluate the returned package. Our problems touch multiple modalities spanning from: textual, categorical, image, to speech data. We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models: XGBoost, BERT, Vision Transformers, Large Language Models.

The Prime Video Science team leverages the latest in machine learning and AI techniques combined with causal inference to bring scientific rigor to the biggest decisions in entertainment: what content to make, what to license, and where to invest. We build large-scale models that simulate how our global customer base responds to change, and we get to see that work shape what the business does. Prime Video is an industry-leading entertainment business and a critical driver of Amazon Prime subscriptions, contributing to customer loyalty and lifetime value. Our models learn from customer behavior to answer questions the business can't test directly and use those answers to guide where Prime Video invests in content and product. We're looking for an Applied Scientist to help build the next generation of these models. As an Applied Scientist on this team, you will build machine learning and deep learning models on large-scale global data to simulate customer behavior across the business. Ideally you bring experience with reinforcement learning, causal inference, and/or the design of agentic AI systems. You will partner closely with business, finance, engineering, and science stakeholders to take ideas from concept and prototype through to production. The candidate should have strong communication skills and the ability to translate data-driven findings into actionable insights. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and the ability to work in a fast-paced and ever-changing environment. Key job responsibilities • Build models that simulate customer behavior to answer counterfactual "what-if" questions that guide major content and product investment decisions. • Apply deep learning, reinforcement learning, causal inference, and experimental design to large-scale customer data to model how customers respond to change. • Design and prototype agentic AI systems and other novel ML approaches and research new methods to improve the accuracy and scale of our models. • Validate and calibrate models against real-world randomized experiments, and partner with software engineers to deliver scalable, production-ready systems. • Translate model outputs into clear recommendations and communicate results to business, finance, and science stakeholders through both technical papers and business-facing documents. About the team The Prime Video Science team is a multidisciplinary group of applied scientists, data scientists, economists, and engineers. We take on some of the hardest research questions in the business, and our work carries visibility up to the CFO/CEO level. We pursue ambitious research at the intersection of machine learning, AI, and causal inference, turning that research into innovations that improve customer experience and strengthen business profitability. Few science teams get to work on problems this hard and this impactful; if that combination excites you, we'd love to talk.

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. Learn more at https://www.amazon.com/music The Data, Insights, Science and Optimization, Music Product and Tech (DISCO MPT) team is looking for a Applied 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 Data Engineers to develop new product based on GenAI technology • Write high quality production code 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.

We are looking for a Principal Applied Scientist to own and advance the scientific vision for WorkSpaces Advisor — our agentic AI system that serves as an always-on troubleshooting companion for workspace administrators and end users. You will define the technical roadmap that transforms Advisor from a recommendation engine into a fully autonomous agent capable of reasoning across complex system states, orchestrating multi-step remediation workflows, and continuously learning from outcomes. This is a leadership role requiring someone who can set the scientific direction for agentic AI in the troubleshooting domain, drive breakthroughs in reasoning and planning under uncertainty, and build the ML foundations that make Advisor the most trusted AI companion in enterprise workspace management. You'll define and drive the scientific strategy for Advisor's agentic capabilities, establishing the research agenda that keeps us at the frontier of autonomous troubleshooting and self-healing systems. Architect agentic reasoning systems that enable Advisor to autonomously diagnose root causes across complex, multi-signal environments — correlating performance telemetry, session behavior, network conditions, and infrastructure state to identify problems before users feel them. Design and build planning and orchestration frameworks that allow Advisor to compose multi-step remediation actions, reason about dependencies and risks, and execute recovery workflows with appropriate human-in-the-loop guardrails. Develop advanced causal inference models that move beyond correlation to true root-cause identification, enabling Advisor to distinguish symptoms from underlying issues across interconnected system layers. Build continuous learning systems where Advisor improves from every interaction — leveraging reinforcement learning from human feedback (RLHF), outcome-driven reward signals, and retrieval-augmented generation (RAG) to expand its troubleshooting knowledge over time. Pioneer natural language reasoning capabilities that allow Advisor to explain its diagnostic process, communicate findings clearly to administrators, and engage in collaborative problem-solving dialogue. Establish evaluation frameworks and safety mechanisms that ensure Advisor's autonomous actions maintain customer trust — defining confidence thresholds, escalation policies, and rollback strategies for automated remediation. Influence the broader organization's AI strategy by identifying opportunities to extend Advisor's agentic patterns to adjacent problem spaces, and by publishing findings that advance the state of the art in autonomous IT operations. Key job responsibilities - Set the scientific vision and long-term research agenda: Define what "best-in-class agentic troubleshooting" looks like scientifically, identify the key unsolved problems, and chart a multi-year path to solving them — securing buy-in from VP-level leadership. - Deliver breakthrough solutions on highly ambiguous problems: Independently identify, frame, and solve novel research challenges in agentic AI for troubleshooting — problems where neither the approach nor the success criteria are pre-defined. - Influence and align across the organization: Drive scientific alignment across product, engineering, and business teams. Translate complex ML concepts into actionable product strategy. Represent the science team in leadership forums and planning cycles. - Build and elevate scientific excellence: Mentor scientists and engineers across the team. Establish best practices for experimentation, evaluation, and deployment of agentic systems. Set the standard for scientific rigor and code quality. - Deliver end-to-end production systems with outsized business impact: Own the full lifecycle from research to deployment for Advisor's core intelligence — making pragmatic trade-offs between long-term invention and near-term delivery while ensuring measurable customer and business outcomes. - Advance the state of the art: Contribute to the external scientific community through publications, patents, and engagement that positions AWS as a leader in autonomous AI operations — bringing outside-in innovation back into Advisor. About the team AWS is on a mission to transform how businesses operate by delivering intelligent, cloud-powered applications. Our Applied AI Solutions organization accelerates customer success through intuitive, differentiated technology that solves enduring business challenges — blending vision with real-world expertise to build turnkey solutions that are easy to adopt and built to scale. Within this organization, we are building the next generation of secure, intelligent workspaces — environments purpose-built for human-AI collaboration at enterprise scale. Our WorkSpaces Advisor is an AI-powered troubleshooting companion that proactively detects, diagnoses, and resolves workspace issues, transforming reactive IT support into intelligent, autonomous problem-solving.

As a Principal Applied Scientist, you are a trusted part of the technical leadership. 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. You solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader. 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. You amplify your impact by leading scientific reviews within your organization or at your location. You scrutinize and review experimental design, modelling, verification and other research procedures. You probe assumptions, illuminate pitfalls, and foster shared understanding. You align teams toward coherent strategies. You educate, keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. You help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads. Key job responsibilities - Responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. - Develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. - Participate in organizational planning, hiring, mentorship and leadership development. - Technically ambitious with a passion for building scalable science and engineering solutions. - You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team Want to be part of the team whose mission is to expand Alexa to new countries, languages, devices and cultures? The Alexa International team makes it happen. Our customers are very diverse in where they live, the languages they speak to Alexa, the devices they use and the content that matters most. In turn our problems are diverse and need innovative solutions. Alexa International is at the nexus of all other Alexa teams and features, such as Smart Home, Music, News, ASR (Automatic Speech Recognition), NLU (Natural Language Understanding), Fire TV, Skills etc. We collaborate with hardware and software developers within and outside Amazon to build delightful experience for our customers. We’re working hard, having fun, and making history; come join us!

Amazon Industrial Robotics is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon Industrial Robotics, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Sr. Applied Scientist in Robot Perception, you will be at the forefront of this transformation. You will develop and deploy state-of-the-art perception algorithms that enable robots to truly understand and interact with the physical world — bridging the gap between theoretical research and realworld impact. Bringing deep expertise in Computer Vision and a nuanced understanding of the capabilities and limitations of modern Vision-Language Models (VLMs), you will innovate boldly and push the boundaries of what's possible. Our vision for the Perception layer is ambitious: to enable seamless, intelligent interaction between the user, the robot, and its environment. This is a rare opportunity to work at the intersection of deep learning, large language models, and robotics — contributing to research that doesn't just advance the field, but reshapes it. You will collaborate with world-class teams pioneering breakthroughs in dexterous manipulation, locomotion, and humanrobot interaction, all at an unprecedented scale. Key job responsibilities Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding • Lead research initiatives in computer vision, sensor fusion and 3D perception • Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities • Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment • Mentor junior scientists and engineers; contribute to a culture of technical excellence • Define and track key metrics to measure perception system performance in real-world environments • Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment • Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations • Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team • Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our Industrial Robotics Group is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.

In Amazon Advertising, we apply Machine Learning at massive scale to optimize programmatic advertising performance. The Demand Tech team owns response prediction and incrementality models that power bid optimization across Amazon DSP and Sponsored Display — determining how billions of ad impressions are valued and served daily across Amazon-owned properties, the open internet, and third-party exchanges. We are looking for a talented Senior Applied Scientist to join our team of scientists and engineers working on high-impact prediction systems that directly drive advertiser KPIs (CPA, ROAS, incrementality) across endemic and non-endemic programmatic advertising. What you will do: Own end-to-end response prediction — design and improve deep learning models for multi-task prediction (click, conversion, page view, incrementality) serving at inference latencies under 10ms at millions of TPS Build and iterate on calibration mechanisms that keep prediction accuracy stable across rapidly shifting supply distributions Integrate novel signals (OpenRTB features, customer behavioral sequences, supply quality feeds) into production models to improve optimization quality Run online A/B experiments at scale, analyze results with statistical rigor, and translate offline gains into measurable business impact Collaborate closely with engineers on model serving infrastructure (SageMaker, GPU inference, real-time feature stores) to deploy models efficiently at scale Mentor scientists on the team and contribute to the broader Amazon ML science community through papers, conferences, and internal deep dives What makes this role unique: Direct business impact: Your models determine bid prices for billions of daily ad impressions — a 1% prediction improvement translates to tens of millions in advertiser value Technical depth at scale: Multi-task deep learning architectures serving real-time inference across multiple global regions under strict latency constraints Diverse problem space: From signal-sparse open internet prediction to calibration under distribution shift, from incrementality measurement to cost-efficient GPU inference Autonomy and ownership: End-to-end ownership from problem framing through research, experimentation, production deployment, and business metric monitoring Impact and career growth: Amazon is investing heavily in building a world-class advertising business. Your work directly influences how Amazon's advertising products optimize campaign performance for advertisers worldwide. You will work with a highly motivated, collaborative team with a broad mandate to experiment and innovate. You will have opportunities to present to senior leadership, define long-term science vision, attend external conferences (NeurIPS, KDD, ICML), and shape the direction of ML-driven advertising at Amazon.