NASA's Orion spacecraft shown splashing down in the Pacific Ocean, west of Baja California, at 9:40 a.m. PST Sunday, Dec. 11.
NASA's Orion spacecraft shown splashing down in the Pacific Ocean, west of Baja California, at 9:40 a.m. PST Sunday, Dec. 11.
NASA

The story behind how Amazon integrated Alexa into NASA’s Orion spacecraft

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

In September 2018, Amazon’s principal solutions architect Philippe Lantin received a call from his manager.

“He said that there was something unique on the horizon, and that their team was being roped into a one-in-a-lifetime opportunity,” says Lantin.

This was no understatement: on the horizon was an opportunity for Amazon to collaborate with Lockheed Martin Space, and integrate Alexa into NASA’s Orion spacecraft. Orion is the first human-rated spacecraft to visit the moon in more than 40 years.

“NASA is trying to engage the public more as we enter this new era of space travel, where we are setting the stage for extra-planetary exploration,” says Lantin. “Given that over 100 million Alexa-enabled devices have already been sold, having Alexa answer questions like 'Alexa, how far to the moon?' and 'Alexa, how fast is Orion going?' is a great way to get people around the world involved in NASA’s missions.”

Setting up an Echo device on Earth is simple: all you need is a Wi-Fi connection and the Alexa app. However, things are far more complicated in space.

“We had several constraints we had to contend with,” says Lantin.
The Alexa team had to operate within a key physical constraint: the shape of the device. The contours of a smart speaker greatly influences it acoustics. To give just one example, the round shape of the Echo Dot offers a full cavity behind the woofer for a better bass response.

Related content
NASA is using unsupervised learning and anomaly detection to explore the extreme conditions associated with solar superstorms.

However, when it came to NASA’s Orion spacecraft, Alexa’s acoustic engineers had to work with what was provided by Lockheed Martin and NASA.

“We were somewhat limited by the form factor, which was a small briefcase-like enclosure that was 1.5 feet by one foot and about five inches in depth.” says Lantin.

There were other physical constraints. Equipment developed for the mission had to be resilient to extreme shocks and vibrations, be at least minimally resistant to radiation emissions in space, and utilize highly specific and custom-built components such as power and data cables.

Limited Internet connectivity

The team also had to deal with issues related to the lack of Internet connectivity. Typically, Echo devices use on-device keyword spotting designed to detect when a customer says the wake word. This on-device buffer exists in temporary memory. After the wake word is detected, the device streams the audio to the cloud for speech recognition and natural language processing.

Orion components

“However, for the Orion mission, our ability to communicate with the Alexa cloud was severely constrained,” says Lantin. “NASA’s spacecraft uses the Deep Space Network to communicate with earth. The bandwidth available to us on the downlink connection is slightly better than dial-up modem speeds with latencies of up to five seconds. To further complicate matters, NASA prioritizes traffic for navigation and telemetry for the first payload — traffic for Alexa was consigned to the secondary payload.”

The team also wanted to demonstrate a fully autonomous experience, one that can be used in future missions where Earth connectivity is no longer a practical option for real-time communications. They used Alexa Local Voice Control to get around the limited internet connectivity. Alexa Local Voice control allows select devices to process voice commands locally, rather than sending information to the cloud.

Lantin says that while the team was motivated by demonstrating technology leadership and scientific innovation in a very challenging environment, the real motivator was making a difference in the lives of millions of customers at home on earth.

“At Amazon, we take pride in delivering customer-focused science,” says Lantin. “That was a huge motivator for us at every step along the way. Consider the innovations we drove to Alexa Local Voice Control. These improvements will allow people on earth to do so much more with Alexa in situations where they have limited or no Internet connectivity. Think about when you are in a car and passing through a tunnel, or driving to a remote camping site. You can do things like tune the radio, turn on the AC and continue to use voice commands, even if you have a feeble signal or no cellular connection.”

Lantin says that the acoustic innovations enabled for Orion will also translate directly into improved listening experiences for people interacting with the mission on earth.

Rohit Prasad, Alexa senior vice president and head scientist, on the initial collaboration with Lockheed Martin

“We are planning to have celebrities, politicians, STEM students and a variety of other personalities interacting with Alexa,” says Lantin. “ And so, we also spent a good deal of time thinking about what people might want to ask Alexa about during the mission.”

The nuances of acoustics aboard Orion

Scott Isabelle is a solutions architect at Amazon. Prior to Amazon, Isabelle was a distinguished member of the technical staff at Motorola, where among other projects, he developed systems for enhancing voice quality in mobile devices, methods for generating adaptive ringtones, and a two-microphone system for noise suppression.

“One of the most important things for a voice AI is being in an environment where it is able to pick up your voice,” says Isabelle.

Related content
Parallel processing of microphone inputs and separate detectors for periodicity and dynamics improve performance.

However, this is easier said than done on Orion, where the conical shape of the space capsule, and its metallic surfaces result in increased reverberation.

“The voice can keep bouncing around losing very little energy. This wouldn’t happen in a typical room where soft material like curtains and sofa cushions can absorb some of the sound. In the capsule, the reverberations off the metal surfaces can play up the wrong frequencies that are critical to automatic speech recognition. This can make it really difficult for Alexa to pick up wake word invocations. ”

Alexa also has to contend with increased noise levels aboard Orion.

NASA | Exploration Mission-1 — pushing farther into deep space

The ideal signal to noise ratio (SNR) for systems involving intelligent voice assistants is in the range 20 to 30 decibels (dB). To place this in context, a SNR of 35 dB is what you would find in a face-to-face conversation between two people standing one meter apart in a typical room (higher SNRs are better). However, the SNR onboard the Orion capsule can be much lower than 20 dB, posing an acoustic challenge.

To enhance the comfort of astronauts during crewed missions, NASA would ordinarily place acoustic blankets to damp down the reverberation in the hard-walled cabin, and some of the noise created by engines and pumps.

“However, because this is an uncrewed mission we have to work within an environment with more reverberation and noise than we would like,” says Isabelle.

re:MARS 2022 — Open space: A revolution in robots for space exploration

There’s another challenge that results from the lack of humans on board. For Orion, commands to Alexa have to be sent from ground control. The low-bandwidth connections utilized for the transmission can make it challenging to transmit voices at the wide range of frequencies essential for differentiating between sounds.

During a typical phone call, our voice is typically transmitted in the narrow band, which ranges from 300 HZ to 3,000 HZ. For Alexa to make out individual words aboard the noisier environment of the space capsule, the voice would have to be transmitted at 8,000 HZ.

“Voice commands from mission control are transmitted to Alexa via a speaker,” says Isabelle. “Flight-qualified speakers are typically designed for narrow-band communications. And so for this mission we were required to use a speaker that could operate in the flight environment.”

Alexa in Space | Alexa Innovators | Build with Alexa

The team relied on what Isabelle calls “brute force” to overcome these acoustic challenges.

Related content
A combination of audio and visual signals guide the device’s movement, so the screen is always in view.

“We designed the speaker playback system to play at extremely loud volumes, which allowed us to increase the SNR to where we wanted it to be.”

The team also took advantage of the physical form factor of Alexa on board to overcome the challenges presented by the noisy environment. The speakers, the light ring and the microphones in the briefcase-like enclosure for Alexa are close to each other, which allows acoustic engineers to overcome some of the obstacles presented by the background noise and reverberation.

Finally, the team deployed two microphones in combination with an array processing algorithm. The latter combined the signals from the two microphones in a way that helps Alexa make sense of the commands being issued from mission control. Because the speakers and microphones are in fixed positions relative to each other — as opposed to a room, where people can be located in any number of locations — the algorithms could be more easily designed to distinguish between speech and the surrounding noise.

Related content
Zoox principal software engineer Olivier Toupet on company’s autonomous robotaxi technology

While the Orion mission will not have any crew members on board, the initial mission will lay the groundwork for Alexa to be integrated into future crewed missions — to the moon, Mars, and beyond. Having Alexa onboard in these future missions would allow crew members to be more efficient in day-to-day tasks, and benefit from the comforts of having Alexa on board such as the ability to play relaxing music and to keep in touch with family and friends back home.

Future crewed missions would have their own unique set of challenges, where Alexa would have to respond to commands from astronauts, who might (literally) be free-floating at multiple points within the capsule. Isabelle and Lantin are already looking forward to overcoming the challenges enabled by crewed missions.

“For someone who grew up watching Star Trek, working on this project has been a dream come true,” says Lantin. “It’s great to be able to build the future. But it’s just as exciting to be able to draw on all of this great work, and be able to enjoy all these new Alexa capabilities during my next vacation, and my day-to-day life right here at home.”

Editor's note

This is a reprint of an article that initially ran on the Alexa Skills Kit Blog. To learn more about the technical innovations that helped get Alexa into space and some inspiring facts about the Artemis I mission, visit the Skills Kit blog.

Research areas

Related content

US, WA, Seattle
Economists in this role partner with business stakeholders to distill complex problems into testable economic questions and generate actionable insights. They collaborate with engineers and scientists to estimate models on large-scale data, design pilots, measure impact, and scale successful prototypes into improved policies and programs. They leverage AI tools to scale economic study for broader business impact. They communicate findings to business leaders, incorporate feedback, and deliver customer-centric solutions at scale.
US, NY, New York
Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Applied Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities As an Applied Scientist on this team, you will: 1. Develop and drive the science strategy for Ads Foundational Model (Ads-FM), aligning it with the program's objectives and overall business goals. 2. Identify high-impact opportunities within Ads-FM program and lead the ideation, planning, and execution of science initiatives to address them. 3. Build and deploy machine learning models using computer vision, natural language processing, and deep learning to evaluate and enhance ad effectiveness. 4. Develop algorithms that extract meaningful signals from image, video, and audio content to predict and improve customer engagement 5. Leverage Amazon's extensive data repository to create predictive models that generate actionable recommendations for more compelling ad creative 6. Collaborate with business leaders and cross-functional teams to implement ML-powered solutions 7. Contribute to the ML roadmap for the Ads-FM program through innovation and research.
US, WA, Seattle
This role will contribute to developing the Economics and Science products and services in the Fee domain, with specialization in supply chain systems and fees. Through the lens of economics, you will develop causal links for how Amazon, Sellers and Customers interact. You will be a key and senior scientist, advising Amazon leaders how to price our services. You will work on developing frameworks and scaleable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Advertising, Supply Chain, Operations, Prime, Consumer Pricing, and Finance. We are looking for an experienced Principal Economist to improve our understanding of seller Economics, enhance our ability to estimate the causal impact of fees, and work with partner teams to design pricing policy changes. In this role, you will provide guidance to scientists to develop econometric models to influence our fee pricing worldwide. You will lead the development of causal models to help isolate the impact of fee and policy changes from other business actions, using experiments when possible, or observational data when not. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. They will work closely with Economists, Data / Applied Scientists, Strategy Analysts, Data Engineers, and Product leads to integrate economic insights into policy and systems production. Familiarity with systems and services that constitute seller supply chains is a plus but not required. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.
US, CA, San Diego
The Private Brands team is looking for a Research Scientist to join the team in building science solutions at scale. Our team applies Optimization, Machine Learning, Statistics, Causal Inference, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon’s retail business and develop Statistical Models and Algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Scientists, Engineers, and Economists. Key job responsibilities You will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable optimization solutions and ML models. This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and economists. As a Research Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. We are particularly interested in candidates with experience in Operations Research and predictive models and working with distributed systems. Academic and/or practical background in Operations Research, Machine Learning and Reinforcement Learning are particularly relevant for this position. To know more about Amazon science, Please visit https://www.amazon.science
US, CA, Palo Alto
Alexa for Shopping (previously Rufus) is seeking a Senior Manager, Applied Science to lead multidisciplinary teams of Applied Scientists and Machine Learning Engineers building next-generation conversational AI and multi-agent systems powering customer-facing experiences at scale. This leader will drive both scientific innovation and execution across large language models (LLMs), agent orchestration, retrieval and grounding systems, evaluation frameworks, and scalable AI infrastructure. The role requires a combination of deep technical judgment, organizational leadership, product and engineering partnership, and operational excellence. The ideal candidate has a strong track record of building high-performing science and engineering teams, translating ambiguous business problems into scalable AI solutions, and delivering measurable customer impact through applied machine learning and generative AI technologies. Key job responsibilities - Lead and grow teams of Applied Scientists and Machine Learning Engineers working on conversational AI and multi-agent orchestration systems. - Define and drive technical strategy for large-scale generative AI systems, including LLM routing, prompting, grounding, memory, tool use, personalization, and response optimization. - Partner closely with Product, Engineering, and Tech leadership to align AI investments with long-term business and customer goals. - Drive end-to-end delivery of production AI systems balancing quality, latency, scalability, safety, and operational reliability. - Establish scientific and engineering best practices across experimentation, evaluation, model iteration, and production deployment. - Lead roadmap prioritization and execution across research innovation and product delivery timelines. - Build scalable evaluation methodologies and quality frameworks for multilingual and global customer experiences. - Mentor and develop technical leaders across both science and engineering disciplines. - Foster a high-performance culture centered on customer obsession, innovation, operational excellence, and strong cross-functional collaboration.
US, NY, New York
We are seeking a Human-Robot Interaction (HRI) Applied Scientist to develop cutting-edge interactions that make robots feel alive, personal, and fun. In this role, you will focus on verbal and non-verbal conversational systems, social dynamics, memory, and long-term relationship formation between robots, their environments, and the people they interact with. Your contributions will be essential in advancing robotics by enabling expressive, socially intelligent, and trustworthy interactions between robots and humans. Key job responsibilities - Develop interactive systems that leverage large language models, multimodal inputs and outputs, reinforcement learning from human feedback, or other advanced techniques to achieve fluid, engaging, and socially appropriate robot behavior - Design and implement intelligent conversational systems that handle turn-taking, grounding, interruption, and incorporates context drawn from a robot's physical environment and shared history with a user - Integrate perceptual sensor streams including gaze, facial expression, gesture, posture, and more to understand social context and produce coherent, lifelike interactions. - Develop memory and personalization systems that allow robots to form lasting relationships with individual users, learn their environments, and adapt their behavior over weeks and months - Stay updated on advancements in HRI, NLP, multimodal AI, and cognitive and social science to apply cutting-edge techniques to robot interaction challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation
IN, KA, Bengaluru
Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies
IN, KA, Bengaluru
Do you want to join an innovative team applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about working with large-scale datasets and developing models that solve real-world fraud and risk challenges? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to help develop scalable machine learning solutions that safeguard millions of transactions every day. In this role, you will partner with senior scientists and engineers to translate business problems into data-driven solutions, build and evaluate models, and contribute to next-generation risk prevention systems, including applications of Generative AI and LLM technologies. Key job responsibilities Apply machine learning and statistical techniques to build and improve risk management models Analyze large-scale historical data to identify risk patterns and emerging trends Develop, validate, and deploy innovative models under the guidance of senior scientists Experiment with emerging technologies, including GenAI/LLMs, to enhance automation and risk evaluation Collaborate closely with software engineers to implement models in real-time production systems Partner with operations and business teams to improve risk policies and operational efficiency Build scalable, automated pipelines for data analysis, model training, and validation Monitor model performance and provide clear reporting on key risk and business metrics Research and prototype new modeling approaches to improve system performance
IN, KA, Bengaluru
Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies
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