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

This role will contribute to developing the Economics and Science products and services in the Fee domain, with specialization in supply chain systems and fees. Through the lens of economics, you will develop causal links for how Amazon, Sellers and Customers interact. You will be a key and senior scientist, advising Amazon leaders how to price our services. You will work on developing frameworks and scaleable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Advertising, Supply Chain, Operations, Prime, Consumer Pricing, and Finance. We are looking for an experienced Principal Economist to improve our understanding of seller Economics, enhance our ability to estimate the causal impact of fees, and work with partner teams to design pricing policy changes. In this role, you will provide guidance to scientists to develop econometric models to influence our fee pricing worldwide. You will lead the development of causal models to help isolate the impact of fee and policy changes from other business actions, using experiments when possible, or observational data when not. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. They will work closely with Economists, Data / Applied Scientists, Strategy Analysts, Data Engineers, and Product leads to integrate economic insights into policy and systems production. Familiarity with systems and services that constitute seller supply chains is a plus but not required. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.

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 Data Scientist to join a team of scientists and engineers who analyze big data, provide analytics and insights and build models and algorithms to power Music product experiences. In this role, you will set the science vision and direction for the team and collaborate with internal stakeholders across product, science and finance to scale and advance our science offerings. You will lead large scale science solutions, prioritize across multiple stakeholders and projects and be part of a fast-paced, dynamic and fun environment. Key job responsibilities • Lead the research and development of models and science products powering personalized recommendations • Partner with product leaders at Amazon Music to develop science-driven business strategies • Partner with science, marketing and product teams across Amazon Entertainment and subscription businesses • Educate internal teams on analytics, insights and measurement • Develop models to determine drivers of key performance metrics, and automate the process of deep diving into variances • Collaborate with product and engineering teams to evaluate the impact of new features or algorithms (e.g., the Playlist Song Recommendation experiments) • Analyze the results of experiments and provide recommendations to optimize solutions • Partner with 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.

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.

Amazon is looking for an Applied Scientist to help build the next generation of sourcing and vendor experience systems. The Optimal Sourcing Systems (OSS) owns the optimization of inventory sourcing and the orchestration of inbound flows from vendors worldwide. We source inventory from thousands of vendors for millions of products globally while orchestrating the inbound flow for billions of units. Our goals are to increase reliable access to supply, improve supply chain-driven vendor experience, and reduce end-to-end supply chain costs, all in service of maximizing Long-Term Free Cash Flow (LTFCF) for Amazon. As an Applied Scientist, you will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including optimization, causal inference, and machine learning/deep learning. Some critical research areas in our space include modeling buying decisions under high uncertainty, vendors' behavior and incentives, supply risk and enhancing visibility and reliability of inbound signals. Key job responsibilities - Set the scientific strategic vision for the team. Lead problem decomposition and roadmap development. - Identify and frame research challenges in ambiguous problem areas; invent novel methodologies to address them. Distinguish between problems requiring novel solutions versus those addressable with existing approaches. - Exercise sound judgment to prioritize between short-term vs. long-term and business vs. technology needs. - Set an example with exemplary scientific analyses; maintainable, well-tested code; and simple, effective solutions. - Drive the design of scientifically-complex software solutions, personally writing critical-path code that embodies scientific novelty. Deploy novel models into production with a track record of impactful delivery. - Develop reusable science components that resolve architecture deficiencies; set standards and drive adoption of state-of-the-art techniques. - Influence team business and engineering strategies. - Communicate effectively with stakeholders to drive alignment and build consensus. - Foster collaborations between scientists across Amazon researching similar problems. Proactively resolve endemic issues where the team's technologies bottleneck other teams. - Actively engage in the development of others, both within and outside the team. - Participate in the science hiring process and engage with the broader scientific community through publications, presentations, and patents.

Are you seeking an environment where you can drive innovation? Do you want to apply inference, advanced statistical modeling and techniques to solve world's most challenging problems in? Do you want to play a crucial role in the future of Amazon's Retail business? Do you want to be a part of a journey that develops a new technology from scratch for answering critical business question in Amazon Retail? Every time an Amazon customer makes a purchase, a number of systems are involved: these systems help optimize acquisition, enable a number of purchase options, ensure great , store products so they are available for fast delivery, and minimize package frustration. The Technology (SCOT) Group develops and manages these systems. We are central to Amazon customers' ability to find what they want and get it when they want it. The Consumer Instock Value (CIV) team within Amazon's Supply Chain Optimization Technology (SCOT) Group develops and manages systems that estimate the long-term impact of inventory availability and delivery speed changes at the product level. Our estimates are crucial inputs for multiple production systems across Amazon's supply chain planning, helping teams make critical decisions about inventory management, selection, and placement. Key responsibilities of an Applied Scientist in CIV Team include: - Developing new statistical, causal, and machine learning techniques and develop solution prototypes to drive innovation - Working with technical and non-technical customers to design model improvements and communicate results - Collaborating with our dedicated software team to create production implementations for large-scale data analysis - Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business - Presenting research results to our internal research community - Leading training and informational sessions on our science and capabilities - Your contributions will be seen and recognized broadly within Amazon, contributing to the Amazon research corpus and patent portfolio. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: Medical, Dental, and Vision Coverage Maternity and Parental Leave Options Paid Time Off (PTO) 401(k) Plan 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!

Are you a passionate scientist in the computer vision area who is aspired to apply your skills to bring value to millions of customers? Here at Ring, we have a unique opportunity to innovate and see how the results of our work improve the lives of millions of people and make neighborhoods safer. You will be part of a team committed to pushing the frontier of computer vision and machine learning technology to deliver the best experience for our neighbors. This is a great opportunity for you to innovate in this space by developing highly optimized algorithms that will work on scale. This position requires experience with developing Multi-modal LLMs and Vision Language Models. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms. Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for computer vision applications. - Research and implement the state-of-the-art computer vision and Vision Language models algorithms. - Collaborate with product managers and engineering teams to design and implement computer vision and machine learning based features for Ring devices - Influence system design and product vision by making informed decisions on the selection of technology, data sources, algorithms, and sensors.

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.

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