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, WA, Bellevue
We are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals.
IN, KA, Bengaluru
Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
GB, London
We are looking for a Senior Economist to work on exciting and challenging business problems related to Amazon Retail’s worldwide product assortment. You will build innovative solutions based on econometrics, machine learning, and experimentation. You will be part of a interdisciplinary team of economists, product managers, engineers, and scientists, and your work will influence finance and business decisions affecting Amazon’s vast product assortment globally. If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you. Key job responsibilities * Work on a challenging problem that has the potential to significantly impact Amazon’s business position * Develop econometric models and experiments to measure the customer and financial impact of Amazon’s product assortment * Collaborate with other scientists at Amazon to deliver measurable progress and change * Influence business leaders based on empirical findings
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide
EG, Cairo
Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models and speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, and UK). Please note these are not remote internships.
US, CA, San Diego
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their macroeconomics and forecasting skillsets to solve real world problems. The intern will work in the area of forecasting, developing models to improve the success of new product launches in Private Brands. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis About the team The Amazon Private Brands Intelligence team applies Machine Learning, Statistics and Econometrics/economics to solve high-impact business problems, develop prototypes for Amazon-scale science solutions, and optimize key business functions of Amazon Private Brands and other Amazon orgs. We are an interdisciplinary team, using science and technology and leveraging the strengths of engineers and scientists to build solutions for some of the toughest business problems at Amazon, covering areas such as pricing, discovery, negotiation, forecasting, supply chain and product selection/development.