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, Seattle
As part of the AWS Applied AI Solutions organization, we have a vision to provide end user applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are easy to adopt and easy to use. The Team Join the next science revolution at AWS Life Sciences Applied AI Solutions, where you'll work alongside world-class scientists to build AI that transforms how therapeutics are discovered, developed, and brought to patients. We're out to revolutionize how medicines are discovered, developed, and brought to patients, powered by a new generation of AI. Our team tackles some of the hardest open problems at the intersection of frontier AI and life sciences. We apply biological foundation models, large language models, and agentic reasoning systems to life sciences problems, then put them into the hands of pharma, biotech, and diagnostics customers as applications and managed services they can fine-tune, tailor, and deploy on their own data. The science challenges are deep: how do you design agentic systems that reason correctly over complex biological, regulatory, and clinical logic? How do you enable customers to tailor foundation models to their proprietary data and get better outputs with less effort? How do you adapt models to reason faithfully in high-stakes scientific and regulatory domains? Today we're focused on two areas. In drug design, our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. In clinical trials, we're building AI that automates and optimizes regulatory and clinical development workflows. We combine frontier research with production-scale delivery to put breakthrough science into the hands of customers solving humanity's hardest problems. We value scientific rigor, encourage publication, and support conference participation. If you want to do research that ships, this is the team. The Role We are seeking an exceptional Principal Applied Scientist to set the scientific direction for our life sciences AI portfolio. You will be the scientific leader who defines research agendas, architects novel approaches, and delivers models and methods that give our customers capabilities that did not previously exist. This is a rare role that combines deep expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will innovate on how large language models reason, plan, and act in complex scientific domains, while applying domain knowledge in biology to ensure models produce scientifically valid outputs. The problems span multiple fronts: - How do you build LLM-based agentic systems that correctly reason over clinical protocols, regulatory standards, and complex multi-step scientific workflows? - How do you develop model customization and training methods that let customers get state-of-the-art results from foundation models? - How do you adapt and extend protein and antibody models so customers can fine-tune on proprietary sequence data and get therapeutically relevant outputs? You will work across drug discovery (protein engineering, antibody design) and clinical trial operations (agentic automation, structured reasoning, domain adaptation). You will own end-to-end scientific solutions from research through production, and your work will directly shape the tools that thousands of scientists use daily. Key job responsibilities - Set the scientific vision and research agenda for LLM reasoning, agentic AI, and biological model customization across the portfolio - Innovate on LLM reasoning, planning, and agentic approaches for complex scientific and regulatory workflows - Develop model customization methods (fine-tuning, RLHF, retrieval augmentation, domain adaptation) that enable customers to train better models on their own data with less effort - Advance methods to adapt and extend biological foundation models for customer-specific therapeutic applications - Solve open research problems in faithful reasoning, multi-step planning, and tool use in high-stakes scientific domains - Partner with Life Sciences domain experts and customers to understand their hardest scientific challenges and translate those into tractable research problems - Publish at top-tier venues and build the team's external scientific reputation - Mentor applied scientists across the team while maintaining significant personal research contribution - Collaborate with product and engineering to ensure research translates into shipped products that serve customers at scale - Influence multi-year research roadmaps through deep scientific expertise and customer understanding A day in the life - Push a new reasoning approach into production that measurably improves outputs for a pharma customer's workflow - Design and run experiments to validate a novel fine-tuning method, then ship it as a capability customers can use immediately - Unblock a delivery milestone by diagnosing why a model is failing on a new class of inputs and implementing a fix - Meet with a customer's scientific team to scope what the next model release needs to do for them - Review a teammate's experimental results, sharpen the approach, and help get it over the finish line - Publish results from shipped work at a top venue, closing the loop between research and impact - Prototype a new idea that could become the next major capability in the product
US, WA, Seattle
We are seeking a Senior Manager, Applied Science to build and lead the science organization across Agentic WorkSpaces. This is a foundational leadership role spanning the full portfolio — Personal, Applications, and Core, and the agentic surfaces (WS4Builders and WorkSpaces for Agents). You will hire, grow, and lead a team of applied scientists who define how we measure and improve the performance of AI agents and human-AI teams. A core part of the role is defining the science agenda itself — identifying which problems are most worth solving and where the highest-leverage bets lie. Directions worth exploring might include Organizational Intelligence (turning institutional knowledge into agent-consumable skills), AI Agent Experience / AiAX (agent observability and autonomous remediation), and contextual, behavioral security that adapts enforcement in real time for human and agent sessions — but these are illustrative examples, not a fixed roadmap, and many other directions are possible. You and your team will define which ones we pursue. The problems your team will solve do not have established industry patterns. You will set the scientific direction and build the team that determines how AI agents and people perceive, reason about, and act reliably within computing environments at enterprise scale. What You Will Do Build and lead the applied science team. Hire, develop, and retain a high-caliber team of applied scientists spanning the Agentic WorkSpaces portfolio. Set the bar for scientific talent, create the growth paths, and build the culture that makes AAWS a destination for the best agent and human-AI researchers. Own the science strategy across the portfolio. Direct the research agenda for how we measure and improve agents and human-AI teams: the benchmarks, task suites, and metrics (accuracy, cost-per-task, task completion, productivity) that turn subjective "it works" judgments into rigorous, reproducible measurement that gates what we ship. Define and drive high-leverage research directions. Work with your team to identify the problems most worth solving and shape the science agenda. Directions worth exploring might include how agents combine deterministic tool use (MCP) with visual reasoning from computer use; Organizational Intelligence and workflow learning (learning from expert recordings, voice annotations, and SOPs); and AI Agent Experience / AiAX (detecting when agents are stuck or degrading productivity and autonomously remediating) — these are illustrative starting points, and your team will weigh them against many other possibilities. Translate science into shipped product. Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype into a decade-old production service operating at massive scale, without compromising the reliability that customers depend on. Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS, and engage directly with enterprise customers on how agent performance, safety, and human-AI productivity are measured and earned. Key job responsibilities Build and lead the applied science team. Hire, develop, and retain a high-caliber team of applied scientists spanning the Agentic WorkSpaces portfolio. Set the bar for scientific talent, create the growth paths, and build the culture that makes AAWS a destination for the best agent and human-AI researchers. Own the science strategy across the portfolio. Direct the research agenda for how we measure and improve agents and human-AI teams: the benchmarks, task suites, and metrics (accuracy, cost-per-task, task completion, productivity) that turn subjective "it works" judgments into rigorous, reproducible measurement that gates what we ship. Define and drive high-leverage research directions. Work with your team to identify the problems most worth solving and shape the science agenda. Directions worth exploring might include how agents combine deterministic tool use (MCP) with visual reasoning from computer use; Organizational Intelligence and workflow learning (learning from expert recordings, voice annotations, and SOPs); and AI Agent Experience / AiAX (detecting when agents are stuck or degrading productivity and autonomously remediating) — these are illustrative starting points, and your team will weigh them against many other possibilities. Translate science into shipped product. Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype into a decade-old production service operating at massive scale, without compromising the reliability that customers depend on. Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS, and engage directly with enterprise customers on how agent performance, safety, and human-AI productivity are measured and earned. Set the long-term scientific vision and team strategy: Define what best-in-class agent performance, evaluation, and learning look like across Agentic WorkSpaces — for computer-using agents and human-AI teams alike. Chart a multi-year research roadmap, and build the team and plan to deliver it. Secure buy-in from VP-level leadership. Hire and grow scientific talent: Own recruiting, calibration, development, and retention for the science team. Mentor scientists toward senior and principal scope, and raise the scientific bar across the organization. Direct research on highly ambiguous, novel problems: Guide the team through foundational challenges in agent perception, reasoning, evaluation, reliability, and human-AI collaboration — problems where neither the approach nor the success criteria are pre-defined. Drive cross-organizational alignment: Work across partner teams (AgentCore, Bedrock model teams, Identity, Security, the MCP ecosystem) and across the Applied AI Solutions product portfolio, with product and engineering leadership, to ensure scientific decisions compose into a coherent product. Deliver measurable business impact: Ensure your team's research translates to customer outcomes: higher task accuracy, lower cost-per-action, faster time-to-production, measurable productivity for human-AI teams, and the trust that lets enterprises scale agent workflows. Establish scientific rigor and operational excellence: Set the standard for experimentation, evaluation, and reproducibility, and the mechanisms that keep the science organization productive and accountable. Advance the state of the art: Enable and champion contributions to the external technical community through publications, patents, and open-source work that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork. About the team AWS Applied AI Solutions' (AAIS) vision is every business innovating with Amazon AI teammates. Our mission is to build delightful AI solutions that improve human capabilities and business outcomes. The Agentic WorkSpaces organization within AAIS envisions a world where people, teams, and AI collaborate securely from anywhere to create unprecedented value for every organization. We build lovable products that empower every business to unlock the full potential of human-AI teamwork, driving smarter decisions, greater creativity, more value, and faster innovation with confidence. Amazon Agentic WorkSpaces (AAWS) is building the world's most lovable, secure, and trusted always-on workspace where AI agents and humans work as partners behind enterprise-grade security. Our portfolio spans persistent desktops (Personal), application streaming (Applications), and Core, and is evolving into the governed operating environment for the hybrid workforce: humans get AI-native desktops for their role, and agents get governed desktops scoped to their task, with administrators managing both as one. This surface includes WS4Builders (an AI-native environment for builders) and WorkSpaces for Agents (W4A) — enabling AI agents to work the way humans do, with access to real applications, real interfaces, and real computing environments. Enterprises want to use AI agents for critical business workloads that touch legacy desktop applications and mainframes, yet 75% of organizations run legacy applications that lack modern APIs, and 90% of corporate data remains locked in systems never designed for agents. Agentic WorkSpaces solves this: it gives enterprises a secure, governed environment where agents and humans operate both legacy and modern applications directly, just as an employee would, without costly migrations.
IN, HR, Gurugram
Work on ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Applied Scientist II, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.
US, NY, New York
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
US, WA, Seattle
Trusted by more startups around the world, AWS makes the power of cloud computing accessible for all by giving founders everywhere access to the same technology that powers the world's largest companies. With nearly two decades of experience supporting hundreds of thousands of startups, including 80% of unicorns, we democratize cloud computing to help founders bring their innovative ideas to life. We support founders at every stage of their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Data is central to how we do this: it helps us identify high-potential startups early, personalize the guidance we deliver, and prioritize where we can create the most value for founders and for AWS. We are seeking an Applied Science Manager to lead a team of applied scientists and analysts building the data and machine learning capabilities behind AWS Startups. You will own the science roadmap end-to-end, from the data foundation that unifies signals about founders, startups, and their products, through a portfolio of machine learning models, to the surfaces that put insights in the hands of the teams and products that serve startups. You will balance hands-on technical leadership with people management, setting the technical bar for your team while developing their careers. Key job responsibilities · Lead, coach, and grow a team of applied scientists, business intelligence engineers, and business analysts; hire and develop talent and set a high technical bar. · Own and prioritize the team's science roadmap and set technical direction for its machine learning models and data assets, balancing rapid experimentation with production quality, cost, and reliability. · Scope scientific projects, design and evaluate experiments, and productionize models that deliver measurable impact, establishing measurement, evaluation, and operational-excellence standards so quality and impact are quantified and defensible. · Drive the science behind recommendation systems, startup segmentation and targeting, and fraud detection, delivering models that surface relevant opportunities, group and prioritize startups by need and fit, and protect the business from fraud and abuse. · Partner with product, engineering, design, and go-to-market teams to translate science into scalable products, and communicate strategy, results, and trade-offs clearly to technical and non-technical leaders. · Foster a culture of scientific rigor and rapid experimentation, and proactively identify and escalate risks with clear mitigation plans. About the team The AWS Startups team builds innovative products and platforms that support startup customers throughout their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Our portfolio serves hundreds of thousands of startup customers globally, and we partner with business development, field marketing, and solutions architecture teams worldwide. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder.
US, CA, Sunnyvale
We are seeking an 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, WA, Seattle
Pricing is one of the most consequential decisions Amazon makes — and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on hard causal identification problems in a high-stakes production environment — and who finds satisfaction in making rigorous methods accessible to a broader team — this role is for you. Key job responsibilities * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation
IN, KA, Bengaluru
Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As a Data Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection by applying advanced statistical techniques and machine learning. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Apply new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)
IN, KA, Bengaluru
The Ads Trust Science team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
PL, Gdansk
Have you ever wondered how we give voice to devices — even when they're offline? The Text-to-Speech on Device team at Amazon builds AI-powered voice models that run locally on hardware with limited resources, serving customers across Alexa, automotive, and accessibility experiences for visually impaired users. We sit at the intersection of speech generation, generative AI, and on-device machine learning, and we're looking for a curious, collaborative Applied Scientist to help us push what's possible. In this role, you will research and develop production-ready speech generation models optimized for constrained environments. You will work across the full model lifecycle — from early experimentation and prototyping through to integration on real devices. If you're excited about solving hard scientific problems that directly improve how millions of people interact with technology, we'd love to hear from you. Key job responsibilities - Design and develop end-to-end machine learning models for on-device speech generation, from early research and experimentation through production-ready deployment. - Research and apply advanced techniques in generative AI, model compression, and knowledge distillation to deliver high-quality voice models within tight hardware constraints. - Propose and validate novel scientific approaches by authoring detailed technical specifications and contributing to peer-reviewed publications when appropriate. - Evaluate model performance rigorously, identify improvement opportunities, and iterate on training and inference pipelines to optimize quality and efficiency. - Collaborate with science and engineering teams across cloud and device platforms to bring speech generation capabilities from research prototypes to integrated product experiences. About the team The Text-to-Speech on Device team builds low-footprint AI models for speech generation that run locally on devices such as Android and FireOS platforms. Our models require significantly less computation than cloud-hosted alternatives, enabling offline voice experiences for Alexa, automotive partners, and accessibility solutions. We work closely with device engineering teams and cloud-based speech science teams to deliver the best possible experience for our customers. Our focus in the coming years is expanding the range of voices and languages we support while continuing to improve naturalness and efficiency on constrained hardware.