Astro’s Intelligent Motion brings state-of-the-art navigation to the home

“Body language” and an awareness of social norms help Amazon’s new household robot integrate gracefully into the home.

At a virtual event today, Amazon’s senior vice president for devices, Dave Limp, unveiled his organization’s new lineup of devices, which included Astro, a household robot with home monitoring and Alexa.

Building a robot that can move intelligently around your home is no easy task. When building self-driving cars or robots for industrial applications, you can predefine high-definition maps of the environments they will encounter on the open road or factory floor. But in the home, nothing is predefined, with frequently rearranged furniture and belongings and people and pets always in motion.

When we set out to build Astro, we knew we wanted its motion to be intuitive and graceful, and we wanted it to be able to interact naturally with humans. That meant that we had to account for the dynamism of the home when deciding on Astro’s design, its sensor configuration, its algorithms, and the speed at which it moves. In addition, we had to deliver Astro at a consumer-accessible price point with a highly optimized suite of sensors and sufficient processing power, when the sensors and processors for other industrial robots that operate at similar speeds can cost thousands of dollars.

So how did we create Intelligent Motion for Astro? 

Astro

Perception and mapping

To be able to move around your home, Astro needs to effectively map its surroundings and understand where it is at any given point: this is perception. Astro’s computer vision system observes the world with both visible and infrared light, which gives it robust perception in dynamic environments and varying lighting conditions. As it perceives where it is in a space, Astro uses its suite of navigation and obstacle sensors as inputs to its on-device simultaneous localization and mapping (SLAM) and obstacle avoidance systems. 

The navigation sensors help identify the positions of key landmarks in 3-D space for the SLAM system, such as corners of tables and doorframes, so that Astro can figure out where it is relative to these landmarks. Astro builds a map of the relative positions of these sparse landmarks when it explores your home and then uses the landmarks to update its location as it moves through the home. 

The obstacle sensors help Astro build a detailed map of its immediate surroundings, capturing the distance to obstacles like couches, chairs, walls, and stairs (see figure below). Astro then uses its knowledge of its position from SLAM and its map of obstacles to path plan and interact with its environment, performing complex tasks such as exploring the home and determining boundaries between spaces, following and approaching people, and figuring out where to hang out. We’ll dive deeper into Intelligent Motion’s SLAM and obstacle avoidance systems in a future science blog post.

Astro point cloud.png
Astro’s Intelligent Motion algorithms build a depth map of Astro's surroundings for mapping and path planning.

Real-time planning

Intelligent Motion is all about having Astro make decisions quickly and autonomously. Homes are ever-changing and full of moving obstacles. For that reason, Astro’s knowledge of its world is rarely perfect, so its navigation system has to be able to handle variability. 

Option testing.png
Astro’s path-planning algorithm tests hundreds of options in real time. Blue arrows indicate longer-range route guidance; colored lines represent options for close-range trajectories within the next three seconds. The colors represent scoring of the trajectories across many weighted factors.

As Astro navigates the home, the Intelligent Motion system generates several hundred potential paths several times a second, evaluates each of them, and then makes a determination on how to move. This process factors in the possibility of changes in the environment (e.g., a book bag dropped on the floor), the desired smoothness of the motion path, and the potential for encountering obstacles.

Astro weighs how each choice contributes toward achieving its current goal, whether that’s reaching a person or heading back to its charger. Astro keeps repeating this process while it is navigating, intelligently optimizing based on its latest knowledge of its world. This approach involves novel methods for dimensionality reduction and probabilistic planning that advance the state of the art in the field of consumer robotics. We’ll also cover this more in a future science blog post.

Body language and communication of intent

Controlling speed, acceleration, and the curvature of Astro’s path are important for making sure Astro can move safely, gracefully, and confidently through the home, but Astro needs to do even more when it interacts with humans. Human-robot interaction (HRI) is a rapidly growing area of research, one that Amazon has invested in in its study of consumer robotics. 

Astro builds trust with customers by moving with predictable behaviors, such as signaling its intents through body language. People and pets do the same thing — signaling, for instance, how they plan to move with a slight turn of the head, change in shoulder angle, or change in eye direction. These are signals people pick up on without even realizing it. 

Emulating these patterns, Astro uses natural changes in head angle as it moves around, indicating which way it is going to turn, pointing at the person it is approaching, and more. When we tested these features, the difference in customer experience with and without them was clear. A simple signal executed via well-coordinated screen and body movements is a powerful tool for communicating intent in real time and making Astro’s behavior more natural.

Moving at humanlike speeds

Astro’s ability to interact naturally with people helps make it even more useful in customers’ homes. Astro can tell when an obstacle is a person and make decisions about how to interact appropriately. To do this, Astro has to operate at human-scale speeds and have an awareness of social norms. 

Socially appropriate distance.png
When following a person, Astro maintains a socially appropriate distance.

For example, when Astro approaches a person, Intelligent Motion uses computer vision signals like the approximate position of that person relative to Astro and the direction the person is facing, the stored map for the area, and other inputs from Astro’s navigation and depth sensors to plan a smooth, graceful path that will enable Astro to end up in front of the person, in the person’s line of sight, at a socially appropriate distance. 

If Astro is following a person, Intelligent Motion helps Astro follow at a comfortable strolling pace for an adult, maintaining a socially appropriate distance, and estimating where that person goes when moving out of view so that Astro can move to a point where the person can be seen and followed again. Astro can determine when an obstacle it detects is a person and follow that obstacle instead of avoiding it, even when it moves in and out of Astro’s field of view. This approach involves dynamic obstacle recognition and tracking, path planning, proxemics, and HRI that we’re excited to share more about soon. 

Recovering from difficult situations

Despite its navigation prowess, Astro will still encounter situations that require it to problem-solve to avoid the need for human intervention. Intelligent Motion includes a set of recovery behaviors that can help when Astro encounters challenges to normal path planning, such as a narrow path that is currently blocked. 

To continue with its task in the face of a blocked path, Astro might try backing up until there is enough space to turn around. As part of this process, Astro also determines when it is time ask for help. We know from our internal testing that people don’t mind occasionally helping Astro, though we have also learned that people have limited patience for a robot that gives up too often and is always asking for help. 

Navigation.png
Astro heads for a gap but is blocked, so the planner calculates new waypoints (blue arrows), and the recovery planner finds a way out and onto the new path.

How Intelligent Motion is designed to protect customer privacy

Moving and reacting quickly requires a very fast system, making local processing of data essential. The raw data from the navigation and obstacle sensors is locally processed into a distance measurement and then discarded, without being sent to the cloud.

When Astro saves a new map at the completion of exploration, information derived from its navigation and depth sensors, including a copy of the 2-D obstacle map, is sent to the cloud, where a map of the home is created and stored. A rendering of the map can then be shown in the Astro app. 

This map contains derived information such as the location of walls, rooms, boundaries, furniture, and objects, plus related data such as customer-provided room names. Map data is encrypted in transit to the cloud, where it is securely stored with 256-bit keys, an industry standard for secure encryption. For more information about the way Astro protects customer privacy, visit amazon.com/astroprivacy. 

What's next?

Astro is Amazon’s first household robot to use Intelligent Motion to gracefully and intuitively interact with people, help customers monitor their homes, bring the power of Alexa to them, and give them back time in their busy lives. 

This is just the beginning for Intelligent Motion, with its navigation and HRI capabilities. We have exciting plans for advancing the science and engineering of Intelligent Motion so that it will improve over time at navigating in homes and serving customers’ needs. We also expect to learn a lot from our customers, who have never had a product quite like Astro in their homes before. Astro’s Intelligent Motion is a brand-new experience that we can’t wait for you to try, and we’re excited to have you join us on the journey.

Research areas

Related content

  • July 10, 2026
    HydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world.
  • Rose Yu
    June 8, 2026
    Four approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments.
  • Meiqi Sun
    April 20, 2026
    Large language models today can solve algebra, pass academic benchmarks, and generate highly structured chain-of-thought explanations. In text-only settings, they often feel startlingly intelligent — methodical, articulate, even strategic. But place those models inside an interactive environment — ask them to click buttons, scroll pages, fill out forms, and submit answers — and their behavior changes. Their careful reasoning falters. They guess where they once deduced. They adhere to templates and produce limited procedural narration: stating what they see and what they will click next, without first forming a structured plan and acting in accordance with plan. It’s as if part of their intelligence has quietly gone offline the moment the cursor appears.
    Machine learning
US, NY, New York
We are seeking a Senior Applied Scientist to develop and optimize Visual Inertial Odometry (VIO) and sensor fusion systems for our intelligent robots. In this role, you will design, implement, and deploy state estimation and tracking algorithms that enable robots to understand their position and motion in real time, even in challenging and dynamic environments. You will own the full pipeline from algorithm development through embedded deployment, ensuring that perception systems run efficiently on resource-constrained robotic hardware. You will also leverage modern machine learning approaches to push the boundaries of classical perception methods, combining learned representations with geometric techniques to achieve robust, real-time performance. This is a deeply hands-on role. You will work directly with sensors, hardware, and real-world data, while prototyping, testing, and iterating in physical environments. The ideal candidate has strong foundations in VIO and sensor fusion, practical experience optimizing algorithms for embedded platforms, and familiarity with how modern deep learning is transforming perception. Key job responsibilities - Design and implement Visual Inertial Odometry algorithms for robust real-time state estimation on robotic platforms like Sprout - Develop multi-sensor fusion pipelines integrating cameras, IMUs, and other sensing modalities for accurate pose tracking - Optimize perception and tracking algorithms for deployment on embedded hardware (e.g., ARM, GPU-accelerated edge devices) under strict latency and power constraints - Apply modern ML-based perception techniques (learned features, depth estimation, neural odometry) to complement and improve classical geometric approaches - Build and maintain calibration, evaluation, and benchmarking infrastructure for perception systems - Collaborate with hardware, controls, and navigation teams to integrate perception outputs into the robot’s autonomy stack - Lead technical projects from research prototyping through production deployment
US, CA, Sunnyvale
We are seeking a Applied Scientist to focus on Robot Navigation. In this role, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. Key job responsibilities - Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles - Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance - Collaborate with world model, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system - Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance - Mentor fellow scientists and engineers while maintaining strong individual technical contributions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products.
US, CA, Sunnyvale
We are seeking a Senior Applied Scientist to focus on Robotics Spatial Intelligence and Semantic Understanding. In this role, you'll research and build advanced semantic and world understanding algorithms that enable robots to observe, understand, and reason about complex and dynamic home environments. You'll work across a broad spectrum of 3D perception, contextual understanding, and world modeling approaches to build robust solutions that support autonomous decision making, task planning, navigation, and manipulation. Key job responsibilities - Develop and implement robust World Understanding and Modeling algorithms for a domestic robot. - Build simulation-based and on-robot evaluation frameworks with comprehensive benchmarks and metrics for systematic evaluation of Our Spatial Intelligence stack. - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world performance. - Collaborate with navigation, manipulation, and other teams to ensure seamless integration of World Understanding capabilities. - Stay current with the latest advances in World Modeling, Spatial Reasoning, and related fields and apply relevant findings to improve system performance About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.
GB, London
Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies — all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business — available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. Prime Video Commerce's mission is to present the right offer to the right customer at the right time — across subscriptions, channels, and transactional video, in every market and on every device. Our science team replaces static business rules with ML-driven decisions that personalise the entire commerce journey, from discovery through checkout and beyond. We operate at scale across hundreds of millions of customers, and we are expanding into new frontiers — combining the latest advances in agentic and generative AI, behavioural simulation, and causal inference to understand the impact of our decisions before they reach customers. We are looking for an Applied Scientist to join the Prime Video Commerce Insights team in London. You will develop and deploy customer-facing models, understand customer behaviour at scale, and explore emerging techniques that help us make better decisions faster. This is a delivery focused role within a high-visibility multidisciplinary group of engineers and scientists, focused on improving the customer experience for Prime Video. Key job responsibilities - Research, design, and implement machine learning approaches (e.g. reinforcement learning and recommendation systems) that personalise across different customer touch points. - Collaborate with engineers to deploy and integrate successful experiment results into large-scale, complex Amazon production systems with low latency. - Design and execute rigorous experiments to demonstrate the technical efficacy and business value of your methods. - Act as a subject-matter expert and help define the science roadmap and research agenda in line with organisational priorities and production constraints. - Provide machine learning thought leadership to technical and business leaders, thinking strategically about business, product, and technical challenges. - Work with technical product managers to work backwards from what matters to customers and deliver ML-backed solutions. - Share results with the team and wider scientific community through documents that are both statistically rigorous and compellingly relevant. A day in the life You will be a research leader and innovator within the Commerce Insights organisation. You will collaborate with talented engineers and senior leaders to solve problems that are uniquely challenging at Amazon's scale: personalising commerce decisions across multiple business lines, balancing competing objectives, and positively impacting hundreds of millions of customers worldwide. The problems here are technically deep — combining large-scale ML, causal reasoning, and behavioural modelling in a domain where every decision carries real revenue and customer-experience consequences. Your research will ship to production and move metrics that matter. About the team You will join a team of engineers and applied scientists with a proven track record of solving highly complex, ambiguous problems — work that has produced patents and publications at top-tier conferences. The team has direct visibility to senior Prime Video leadership and collaborates broadly across Commerce, Content, and Platform teams to shape how customers discover, subscribe to, and engage with video content. This is a team that operates at the intersection of rigorous research and real-world impact, where your ideas move from whiteboard to production for hundreds of millions of customers.
US, WA, Seattle
AWS is seeking a Data Scientist to join our Consumer Experience team. We're looking for someone who is curious about large-scale data challenges and motivated to apply ML/AI and advanced analytics to solve real customer problems. Reporting to the Science Lead, this role supports the analytical capabilities of AWS by applying emerging technologies, contributing to data science best practices, and partnering with stakeholders to deepen understanding of the AWS customer experience. The ideal candidate has experience working in complex data and analytics environments, with hands-on skills in Machine Learning and advanced analytics techniques — preferably within cloud computing. You should be comfortable building models and equally comfortable explaining what they mean to a non-technical audience. Key job responsibilities - Apply statistical analysis, ML, and complex data mining techniques to deliver insights that shape the AWS customer experience. - Build production-ready analytics solutions that support effective business decisions. - Partner with product, engineering, and design teams to develop and launch data products. - Contribute to the adoption of Gen AI and ML-based approaches across the data science practice, including building proof-of-concept AI-driven customer use cases. - Communicate technical findings clearly to both technical and non-technical stakeholders. - Bring new ideas — share approaches, tools, and methods that raise the bar for the team.
US, CA, Santa Monica
MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Applied Scientist III Job Location: Santa Monica, California Job Number: AMZ10564053 Position Responsibilities: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. 40 hours / week, 8:00am-5:00pm, Salary Range: $167,100/year to $226,100/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000
US, VA, Arlington
PXT Central Science is seeking an exceptional Data Scientist to join our team. The ideal candidate will thrive in a dynamic, multifaceted role where you'll translate complex business challenges into rigorous quantitative frameworks, extract actionable insights from structured and unstructured datasets, and architect science-backed, scalable solutions that elevate the experience of our 1 million+ employees worldwide. If you're energized by the opportunity to apply data science to our mission of making Amazon Earth's Best Employer, we want to hear from you. Key job responsibilities • Own the design, development, and maintenance of scalable models and prototypes leveraging statistical, machine learning, or GenAI methodologies to enhance employee experience. • Partner with scientists, engineers, and product leaders to solve for employee experience defects using scientific approaches, building new services and tools that deliverable measurable impact. • Author and maintain detailed technical documentation related to the projects you drive. • Communicate results to diverse audiences of varying technical background with effective writing, visualizations, and presentations • Stay current with emerging methods and technologies, and implement them strategically to amplify the team’s impact. About the team The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, machine learning, and Generative AI to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science, engineering, and UX to develop and deliver solutions that measurably achieve this goal.
US, WA, Seattle
Are you passionate about using data and machine learning to improve how Amazon reaches customers? The Customer Targeting team is looking for an Applied Scientist to build the science behind the targeting of Amazon's marketing initiatives across channels and ad formats. You will turn business questions into modeling problems and take your solutions from prototype to production, working closely with senior scientists, engineers, and business partners. We are moving targeting toward outcome-based optimization and an AI-native, agentic way of working. In this role you will work on online optimization and reinforcement learning, incrementality modeling, and the next generation of our segment-based targeting model. You will also help build agentic workflows that make targeting decisions transparent and bring science recommendations to the people using them. The right candidate has strong quantitative and statistical skills, hands-on experience with modern ML methods, and is comfortable working through ambiguity in a fast-paced environment. You pay close attention to detail, learn quickly, and work well in a highly collaborative team. Key job responsibilities - Build online optimization (e.g., TSO, bandits) and reinforcement learning methods that optimize targeting decisions for business outcomes rather than proxy metrics. - Develop incrementality and uplift models that estimate the causal impact of targeting, and use them to improve who we target and how much we spend. - Improve the existing segment-based targeting model: engineer and evaluate new features, migrate it to new model architectures, and build automated feedback loops that retrain and improve the model from observed outcomes. - Contribute to the intelligence layer of our agentic system: build components that explain targeting decisions and propose science-driven solutions to business partners. - Design and analyze experiments (A/B tests, causal inference methods) to measure the incremental impact of targeting changes. - Write production-quality model code and partner with engineers to deploy, monitor, and maintain models. - Present methods and results clearly, backed by data, with actionable recommendations for science and business stakeholders. About the team Predictive Targeting builds the systems that decide who sees Amazon's marketing content, and how often, across Amazon's stores and channels. We are engineers and scientists working on one shared problem: making sure customers see the most relevant and engaging marketing, and making each campaign better than the last.
US, WA, Seattle
The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art 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. Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing in Pasadena, CA, is looking to hire an Applied Scientist on the Materials team, focused on materials integration and process development for superconducting quantum processors. You will join a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have expertise in thin-film deposition, semiconductor integration processes, and materials characterization, with a strong understanding of how device topography and process sequences constrain materials integration choices. Candidates with a track record in materials integration for superconducting or semiconductor devices at wafer scale will be preferred. We are looking for candidates with strong scientific and engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. As an Applied Scientist at CQC, you will be expected to drive materials integration R&D from process development through hardware demonstration and stay abreast of advances in superconducting materials and semiconductor manufacturing. Key job responsibilities You will develop and optimize materials for superconducting quantum hardware, with an emphasis on thin-film deposition processes and materials solutions that enable integration architectures for scaling qubit density. This includes establishing process-structure-property relationships for production-relevant films and interfaces, qualifying new tools and process modules, and designing integration schemes compatible with larger wafer formats. You will coordinate with cross-functional teams to translate materials advances into production, and publish results in scientific journals when appropriate.