Orbital Systems makes sustainable water use something people can enjoy

Mehrdad Mahdjoubi, founder and CEO of Alexa Fund portfolio company, explains "no compromise" approach to saving resources without sacrificing user experience.

(Editor’s note: This article is the latest installment in a series by Amazon Science delving into the science behind products and services of companies in which Amazon has invested. The Alexa Fund invested in Orbital Systems in April 2022.)

Americans use an average of 60 gallons of clean water per person inside their homes each day, nearly half of which goes to toilets and showers. Low-flow fixtures and other conservation strategies have reduced per-capita consumption since the 1980s. But the scarcity of water on Earth — less than 1% of the water on our planet is drinkable — demands that we use it more wisely.

Orbital Systems founder and CEO Mehrdad Mahdjoubi
Orbital Systems founder and CEO Mehrdad Mahdjoubi said his work with NASA on a plan for human habitation on Mars inspired his thinking when he launched Orbital.

Orbital Systems aims to meet this demand with products inspired by a setting where water is even more scarce: Mars.

As a master’s student in industrial design, founder and CEO Mehrdad Mahdjoubi collaborated with NASA scientists on a plan for long-term human habitation on Mars.

“The limitations on available resources meant that we had to be creative,” Mahdjoubi says.

He realized that other essential resources, like energy and nutrients, tend to flow in a circular manner. “With energy, we have the sun. Nutrients cycle between the physical environment and living organisms. But water use is not like that,” Mahdjoubi explains.

Mahdjoubi started Orbital Systems in 2012 to develop resource-saving products for consumers on Earth. The Orbital Shower was the first product to launch. The shower starts with less than a gallon of water, and the system checks the water quality 20 times per second during operation. Water too contaminated to be reused is discarded and replaced, and the rest is filtered and exposed to ultraviolet light before being recirculated. Because the recirculated water is warm, it requires much less energy for heating. The Orbital Shower uses up to 90% less water and 80% less energy than a conventional shower.

Next came the Orbital Tap, which reuses water from a sink to flush a toilet. “It’s a solution to the age-old problem of flushing clean drinking water down the toilet,” Mahdjoubi says.

Sustainability
Pioneering web-based PackOpt tool has resulted in an annual reduction in cardboard waste of 7% to 10% in North America, saving roughly 60,000 tons of cardboard annually.

Orbital products are available to hotel chains, real-estate developers, and individual consumers in Sweden, Denmark, and Germany. Mahdjoubi is seeking partners and installers to enable expansion to markets in North America and beyond.

Orbital users can start a customized shower — lighting, music, flow, temperature, duration, etc. — with a single command via an Alexa integration.

Mahdjoubi spoke with Amazon Science about water use from Mars to ancient Rome to our own bathrooms and what differentiates Orbital Systems’ products from other resource-saving strategies.

  1. Q. 

    What inspired you to design sustainable water systems for Mars and implement them on Earth?

    A. 

    While I was studying industrial design at Lund University, I had the opportunity to go to Johnson Space Center and take part in a project with NASA. The goal was to enable an earthly living standard on Mars.

    Alexa in space
    From physical constraints to acoustic challenges, learn how Amazon collaborated with NASA and Lockheed Martin to get Alexa to work in space.

    Establishing a Mars colony required us to solve a lot of issues related to resource management, given the strict resource limitations.

    There are three resources that humans need in addition to oxygen. One is energy, the second is water, and the third is nutrients. I started looking at how we handle resources on Earth, and how we might translate the positive aspects to a new setting without repeating the more foolish aspects.

    Every resource has a supply side and a demand side. In the energy sector, we’ve walked a pretty long way on the demand side. A hundred or two hundred years ago, all of our focus in energy was on the supply side: pump up more oil, pump up more gas, produce more. Then around the second oil crisis in the ‘70s, there was a massive realization that we can’t just focus on pumping up more, creating more energy. We need to think about how we use it.

    The Orbital Shower mobile app is seen on a smartphone screen displaying 370 liters of water saved, the phone is sitting on a towel
    Orbital's CEO says their system "starts with technical innovation that actually reduces water and energy use and then tracks the savings through a digital interface."

    Fast forward to today, we have much more focus on the demand side. There’s an understanding that we can do a lot more if we just don’t waste the energy we make. Many of the products we buy are energy efficient: fridges, TVs, LED lights.

    Then I looked at water and found the way we use water now is practically no different from the Roman aqueducts of 2,000 or 3,000 years ago. We find water somewhere, and if it’s clean, we pump it to houses. If it’s not clean, we treat it first. We haven’t really changed anything since the Romans. I mean, we flush toilets with drinking water. We haven’t done anything to optimize the demand side.

    So when it comes to building a new habitat on Mars, what are we not going to do? We’re not going to generate drinking water — which we do out of air, pretty expensive — and then pour it down a drain or flush it down a toilet.

    That was the background, back in 2012. At the time, the mission launch was set to 2035 and the shower project was mostly at the conceptual level. I felt there was no reason to wait 20 years to develop a product for eight astronauts when there is an urgent need and much bigger opportunity on Earth.

    I moved back to Sweden, where I was born and raised, started Orbital Systems, and got research funding to come up with functioning prototypes. Today we’ve raised north of a hundred million dollars and have a team of almost 100 people.

  2. Q. 

    How did you approach the product design, and what were the biggest challenges?

    A. 

    What attracted me as a product designer is that this is a rare "no compromise" solution. You can save water and energy and you get a really, really nice shower experience. If you were to ask yourself what constitutes a nice shower, it comes down to three factors. Number one is clean water, number two is flow rate, and number three is temperature stability. We outperform conventional showers on all three points.

    Achieving sustainability
    How data-driven methods can help to identify fault detection and drive energy efficiencies for facilities of all sizes.

    One challenge in creating that experience was to make everything seamlessly work together. We’re talking about 350 individual components. It’s a multidisciplinary system where you have to control everything, including thermodynamics, software, pumping fluid dynamics, temperature sensors, pressure sensors, filtration, and electronics. We had to develop our own water quality sensors, figure out how to handle soap, and those kinds of things.

    And we wanted to hide the tech. People want to feel the bathroom is a nice relaxing area, not a tech lab. So we needed to spend the time and energy to make it invisible. In an Orbital Shower, aside from the control dial and digital display, there’s no way you would guess what’s going on in the background.

    Adapting to customer needs
    Scientists updated the system to accurately measure body fat percentage and create personalized 3D models even if there’s not enough room to take a full-body photo.

    A key technical challenge that we had to overcome was filtration. Most filters that can trap bacteria and viruses are exceedingly slow. We use filtration technology, developed with NASA funding, that is ultra-effective but very fast, coupled with ultraviolet light for disinfection.

    Another challenge was that it needed to be easy to install. We wanted to make sure that our products could fit in any bathroom, whether the wall is made of bricks or plaster. A lot of effort was spent to accommodate different circumstances and building methods. We offer retrofit models that can be installed in existing bathrooms, as well as models meant for new installation.

  3. Q. 

    How is Orbital technology different from other ‘smart water’ systems?

    A. 

    First, if you look into water technologies in general, the majority has been done at the utility level, like desalination plants, water treatment plans, that kind of stuff. Much less has been done for the end consumer, and most of that has targeted drinking water, which is a tiny fraction of the water we use.

    That said, technology for low-flow showers and toilets has existed for like 40 years or so and still not become super popular, because the quality of the experience is compromised. We are going at it the other way. I think, personally, to find scalable solutions, we need to focus on the ‘no compromise’ ones.

    Then there are smart water systems that are all about data, informing consumers about their water use with the goal of changing behaviors to save water. Several of our clients told us they had tried such ‘awareness solutions’ before but fell into despair, because they felt they couldn’t do enough.

    An Orbital Shower control dial with a digital reading showing 91.3 liters saved is seen, a person's hand is seen is pointing to the dial
    The Orbital Shower starts with less than a gallon of water, and the system checks the water quality 20 times per second during operation.

    Orbital starts with technical innovation that actually reduces water and energy use and then tracks the savings through a digital interface.

    The digital interface also features an Alexa integration where you can start your perfect shower with a single command, coordinating the Orbital Shower with other smart-home features like lighting, room temperature, window dressings, music, et cetera.

    I think people want to maximize their experience — like taking a long shower — without being wasteful, to be responsible and live sustainably but also have a pleasant experience. Why shouldn’t we have both?

Research areas

Related content

US, CA, San Francisco
Are you interested in a unique opportunity to advance the accuracy and efficiency of Artificial General Intelligence (AGI) systems? If so, you're at the right place! We are the AGI Autonomy organization, and we are looking for a driven and talented Member of Technical Staff to join us to build state-of-the art agents. As an MTS on our team, you will design, build, and maintain a Spark-based infrastructure to process and manage large datasets critical for machine learning research. You’ll work closely with our researchers to develop data workflows and tools that streamline the preparation and analysis of massive multimodal datasets, ensuring efficiency and scalability. We operate at Amazon's large scale with the energy of a nimble start-up. If you have a learner's mindset, enjoy solving challenging problems and value an inclusive and collaborative team culture, you will thrive in this role, and we hope to hear from you. Key job responsibilities * Develop and maintain reliable infrastructure to enable large-scale data extraction and transformation. * Work closely with researchers to create tooling for emerging data-related needs. * Manage project prioritization, deliverables, timelines, and stakeholder communication. * Illuminate trade-offs, educate the team on best practices, and influence technical strategy. * Operate in a dynamic environment to deliver high quality software.
IN, KA, Bangalore
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed, Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon. Candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes. This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences
US, NY, New York
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in the Sponsored Products organization builds GenAI-based shopper understanding and audience targeting systems, along with advanced deep-learning models for Click-through Rate (CTR) and Conversion Rate (CVR) predictions. We develop large-scale machine-learning (ML) pipelines and real-time serving infrastructure to match shoppers' intent with relevant ads across all devices, contexts, and marketplaces. Through precise estimation of shoppers' interactions with ads and their long-term value, we aim to drive optimal ad allocation and pricing, helping to deliver a relevant, engaging, and delightful advertising experience to Amazon shoppers. As our business grows and we undertake increasingly complex initiatives, we are looking for entrepreneurial, and self-driven science leaders to join our team. Key job responsibilities As a Principal Applied Scientist in the team, you will: * Seek to understand in depth the Sponsored Products offering at Amazon and identify areas of opportunities to grow our business via principled ML solutions. * Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in ML. * Design and lead organization wide ML roadmaps to help our Amazon shoppers have a delightful shopping experience while creating long term value for our sellers. * Work with our engineering partners and draw upon your experience to meet latency and other system constraints. * Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver. * Be responsible for communicating our ML innovations to the broader internal & external scientific community.
CA, BC, Vancouver
Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA), you'll have the opportunity to reinvent the hiring process and deliver unprecedented scale, sophistication, and accuracy for Amazon Talent Acquisition operations. ITA is an industry-leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise. Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time. You’ll work on state-of-the-art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in-house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives. Together, we can solve the world's toughest hiring problems. Global Hiring Science owns and develops products and services using Artificial Intelligence and Machine Learning (ML) that enhance recruitment. We collaborate with scientists to build and maintain machine learning solutions for hiring, offering opportunities to both apply and develop ML engineering skills in a production environment. Key job responsibilities • Design and implement advanced AI models using the latest LLM and GenAI technologies to develop fair and accurate machine learning models for hiring. • Clearly and cogently present your work and ideas, and respond effectively to feedback. • Collaborate with cross-functional teams with Research Scientists and Software Engineers to integrate AI-driven products into Amazon’s hiring process. • Stay at the advance of AI research, continuously exploring and implementing new techniques in NLP, LLMs, and GenAI to drive innovation in hiring. • Implement advanced natural language processing models to extract insights from diverse data sources. • Ensure effective teamwork, communication, collaboration, and commitment across multiple teams with competing priorities. • Contribute to the scientific community through publications, presentations, and collaborations with academic institutions. About the team The mission of Global Hiring Science (GHS) is to improve both the efficiency and effectiveness of hiring across Amazon with assessments and interview improvements. We are a team of experts in machine learning, industrial-organizational psychology, data science, and measuring the knowledge, skills, and abilities that it takes to be successful at Amazon.
US, CA, San Francisco
Amazon has launched a new research lab in San Francisco to develop foundational capabilities for useful AI agents. We’re enabling practical AI to make our customers more productive, empowered, and fulfilled. In particular, our work combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments. Our research builds on that of Amazon’s broader AGI organization, which recently introduced Amazon Nova, a new generation of state-of-the-art foundation models (FMs). Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. We’d love for you to join our lab and build it from the ground up! Key job responsibilities You will contribute directly to AI agent development in an applied research role, including model training, dataset design, and pre- and post-training optimization. You will be hired as a Member of Technical Staff.
US, WA, Seattle
PXTCS is looking for an economist who can apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure impact, and transform successful prototypes into improved policies and programs at scale. PXTCS is looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. A day in the life The Economist will work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team The People eXperience and Technology Central Science (PXTCS) team uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. PXTCS is an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.
US, CA, San Francisco
The Amazon General Intelligence “AGI” organization is looking for an Executive Assistant to support leaders of our Autonomy Team in our growing AI Lab space located in San Francisco. This role is ideal for exceptionally talented, dependable, customer-obsessed, and self-motivated individuals eager to work in a fast paced, exciting and growing team. This role serves as a strategic business partner, managing complex executive operations across the AGI organization. The position requires superior attention to detail, ability to meet tight deadlines, excellent organizational skills, and juggling multiple critical requests while proactively anticipating needs and driving improvements. High integrity, discretion with confidential information, and professionalism are essential. The successful candidate will complete complex tasks and projects quickly with minimal guidance, react with appropriate urgency, and take effective action while navigating ambiguity. Flexibility to change direction at a moment's notice is critical for success in this role. Key job responsibilities - Serve as strategic partner to senior leadership, identifying opportunities to improve organizational effectiveness and drive operational excellence - Manage complex calendars and scheduling for multiple executives - Drive continuous improvement through process optimization and new mechanisms - Coordinate team activities including staff meetings, offsites, and events - Schedule and manage cost-effective travel - Attend key meetings, track deliverables, and ensure timely follow-up - Create expense reports and manage budget tracking - Serve as liaison between executives and internal/external stakeholders - Build collaborative relationships with Executive Assistants across the company and with critical external partners - Help us build a great team culture in the SF Lab!
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As an Applied Scientist, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Senior Applied Scientist, you'll spearhead the development of breakthrough foundation models and full-stack robotics systems that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll combine hands-on technical work with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. You'll leverage Amazon's vast computational resources to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Lead technical initiatives across the robotics stack, driving breakthrough approaches through hands-on research and development in areas including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Guide technical direction for full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack - Influence technical decisions and implementation strategies within your area of focus A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Guide fellow scientists in solving complex technical challenges across the full robotics stack - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within your team and with key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster and extensive robotics infrastructure - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, CA, San Francisco
Amazon AGI Autonomy develops foundational capabilities for useful AI agents. We are the research lab behind Amazon Nova Act, a state-of-the-art computer-use agent. Our work combines Large Language Models (LLMs) with Reinforcement Learning (RL) to solve reasoning, planning, and world modeling in the virtual world. We are a small, talent-dense team with the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. Come be a part of our journey! --- About the team We’re looking for a generalist software engineer to build and evolve our internal data platform. The team builds data-intensive services that ingest, process, store, and distribute multi-modal training data across multiple internal and external sources. This work emphasizes data integrity, reliability, and extensibility in support of large-scale training and experimentation workloads. The team also builds and maintains APIs and SDKs that enable product engineers and researchers to build on top of the platform. As research directions change, so does our data, and today the team is focused on hardening the platform to reliably deliver an evolving set of data schemas, sources, and modalities. By building strong foundations and durable abstractions, we aim to enable new kinds of tooling and workflows over time. The team will play a key role in shaping them as the research evolves. --- Key job responsibilities * Build and operate reliable, performant backend and data platform services that support continuous ingestion and use of multi-modal training data. * Identify and implement opportunities to accelerate data generation, validation, and usage across training and evaluation workflows from multiple internal and external sources. * Partner closely with Human Feedback, Data Generation, Product Engineering, and Research teams to evolve and scale the data platform, APIs, and SDKs. * Own projects end to end, from technical design and implementation through deployment, observability, and long-term maintainability. * Write clear technical documentation and communicate design decisions and tradeoffs to stakeholders across multiple teams. * Raise the team’s technical aptitude through thoughtful code reviews, knowledge sharing, and mentorship.