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.

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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.

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    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.

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    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.

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    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?

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Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists
US, CA, San Francisco
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As an Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. About the team We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities
US, CA, San Francisco
Amazon is seeking an exceptional Sr. Applied Scientist to lead the development of perception systems that harness the power of radar and thermal imaging — enabling robots to perceive and operate reliably in conditions where conventional vision alone falls short. In this role, you will develop ML-driven perception pipelines for non-traditional sensing modalities, pushing the boundaries of what robots can see, understand, and act upon in challenging real-world environments. At Amazon, we leverage advanced robotics, machine learning, and artificial intelligence to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence. As a Sr. Applied Scientist in Multi-Modal Perception, you will apply deep computer vision expertise alongside classical signal processing techniques for radar and thermal imaging — modalities that provide robustness in adverse conditions and sensing capability beyond the visible spectrum. You will develop ML-based methods to extract semantic and geometric information from radar point clouds, radar tensors, and thermal imagery, and fuse these with camera and depth data to build perception systems that are reliable, comprehensive, and ready for deployment at scale. Your work will unlock new capabilities for our robots — enabling reliable detection, classification, and scene understanding in low-visibility conditions, cluttered environments, and scenarios where traditional RGB-based perception is insufficient. You will lead research that translates cutting-edge advances in deep learning and computer vision to these underexplored but high-impact sensing modalities. Join us in building the next generation of multi-modal perception systems that will define the future of autonomous robotics at scale. Key job responsibilities - Lead the research, design, and development of ML-based perception pipelines for radar and thermal/infrared imaging modalities - Develop deep learning models for object detection, classification, segmentation, and tracking using radar data (point clouds, range-Doppler maps, radar tensors) and thermal imagery - Design and implement multi-modal fusion architectures that combine radar, thermal, camera, and depth data for robust, all-condition perception - Develop novel representations and feature extraction methods tailored to the unique characteristics of radar and thermal sensors (sparsity, noise profiles, spectral properties) - Build end-to-end perception systems — from raw sensor data processing and calibration to model training, evaluation, and real-time deployment - Collaborate closely with Hardware, Navigation, Planning, and Controls teams to define sensor configurations and deliver integrated autonomy solutions - Establish benchmarks, datasets, and evaluation frameworks for radar and thermal perception - Mentor scientists and engineers; foster a culture of scientific rigor, innovation, and high-impact delivery - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.