How Amazon scientists are driving success for Alexa in the car

From noisy cars to unreliable signals, researchers have worked to extend the Alexa experience to vehicles on the move.

“Alexa, where’s the nearest coffee shop?”

In vehicles with Alexa, drivers can pose questions like that — while keeping their eyes on the road and hands on the wheel. Amazon’s cloud-based voice assistant technology works with the car’s navigation system to figure out where the nearest coffee shop is and guide drivers to it.

AlexaAuto_V2_GIF (1).gif
Alexa’s local voice control of native features in the car such as adjusting the air conditioning, tuning the radio, opening the sunroof, and turning on the reading light do not require a connection to the cloud.

Interactions like this hinge on the ability of the built-in hardware and software to accurately detect when Alexa is invoked.

“We want to take the Alexa experience that customers are familiar and comfortable with in the home and extend that experience to cars,” said Jinghao Liu, a principal hardware engineer with Amazon Smart Vehicles at the team’s vehicle lab in San Jose, California.

The challenge is that cars are a different acoustic and operating environment than a home. They are noisier, for starters. What’s more, the bulk of customer needs in a car are location-specific and tied to real-time road, traffic, and weather conditions. And, as most drivers know, cellular service in a car is never 100% reliable, especially outside of cities and towns.

Related content
Dataset that requires question-answering models to look up multiple facts and perform comparisons bridges a significant gap in the field.

There’s another complication: the hardware. Amazon scientists often work with car manufacturers on the design and implementation of their systems, but during validation in the 6,000-square-foot vehicle lab, there is one element they cannot control for.

“We are giving our partners access to Alexa services and functionalities, but we don’t make the device,” explained Parimal Tathavadekar, a senior manager on the Amazon Smart Vehicles team. “We make sure that the integration of our technology with their device is correct and happily working.”

Tackling road noise

Perhaps the biggest difference between speaking to Alexa in a car versus at home is the increase in noise, Liu noted. Think of the rumble from tires on the road and turbulence from the wind, as well as whatever radio station, podcast, or music is playing through the stereo.

To invoke Alexa, the car’s microphones need to pick out the speaker’s voice from this din and send the voice signal to the automatic speech recognition model running in the cloud.

AlexaAuto_V1_GIF.gif
Inside Amazon’s vehicle lab, engineers simulate vehicle locations, validating that Alexa is integrated with the car’s navigation system and able to provide relevant information and experiences to customers on the go.

In design meetings, Amazon scientists collaborate with car manufacturers on techniques that allow the car’s built-in system to focus on the person speaking to Alexa and cancel out acoustic echo and background noise.

For example, Amazon scientists recommend car manufacturers use a signal-processing technique called beamforming that steers microphone arrays toward a voice signal coming from a particular source, such as the driver, while suppressing audio interference from all other directions.

“You are laser focusing on just that one angle,” Liu explained, adding that beamforming is used in the Amazon Echo family of devices.

The science behind Alexa
Combining psychoacoustics, signal processing, and speaker beamforming enhances stereo audio and delivers an immersive sound experience for customers.

Another recommended technique called acoustic echo cancellation blocks out the noise from the car’s speakers so that music played by the speaker does not interfere with the customer requests. Manufacturers can also deploy various noise reduction algorithms, which learn to separate a speaker’s voice from other background noise.

Inside the vehicle lab, Amazon scientists use software and acoustic tools to simulate road noise, music, and other acoustic elements as they test and validate Alexa integration with the car manufacturer’s built-in systems.

To achieve certification, the built-in system must stream a voice signal to the automatic-speech-recognition model that’s of a similar quality to the voice signals streamed from any of the other hundreds of millions of Alexa-connected devices around the world.

“This model is serving millions of devices at home and on the go, so we make sure that the received audio signal is reasonable, and our cloud takes care of the rest,” Liu said.

Personal mobility

Customer interactions with Alexa in cars are often location-specific, ranging from queries about points of interest along the way to driving directions that avoid traffic jams.

Amazon does not have its own navigation system or a proprietary database of points of interest, noted Tathavadekar.

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.

Rather, Alexa AI is designed to resolve a speaker’s query and hand off such intents to the car’s navigation engine for geolocation information. Alexa uses this data to interact with third-party databases for information about points of interest.

The use of Alexa in conjunction with a car’s native navigation system enables drivers to accomplish tasks via voice instead of “having to type or touch through different navigation screens, which can be a distraction,” Tathavadekar said.

The contextual awareness also allows Alexa to deliver experiences such as tuning in to local radio stations on a cross-country road trip.

Inside Amazon’s vehicle lab, engineers simulate vehicle locations, Liu explained, validating that Alexa is integrated with the car’s navigation system and able to provide relevant information and experiences to customers on the go.

Staying connected

Quality of connection is another factor in how quickly a device can connect to Alexa and get a response.

“When you’re driving, you’re going to go through a patch with poor reception,” Liu noted. That’s why he and his colleagues also test how built-in systems handle scenarios with poor, or no, cellular signal.

In today’s world, customers are used to getting a quick response from Alexa at home, Liu noted. If the response takes longer on the road, the customer may think the device is defective. That’s why Amazon makes sure that Alexa stays connected to customers, even when the cellular signal is lost.

For example, Alexa’s local voice control of native features in the car such as adjusting the air conditioning, tuning the radio, opening the sunroof, and turning on the reading light do not require a connection to the cloud.

In the vehicle lab, the team measures the built-in hardware device’s latency with various levels of connectivity. If the cellular signal strength is strong, but Alexa is slow to respond, that may be an indication that the hardware needs to be re-engineered.

As connectivity becomes ubiquitous, Liu added, the latency expectations will continue to tighten. Amazon scientists also expect that experiences within and outside of cars will become more engaging and complex going forward.

“Every year, we’re adding new features and equipment to our test arsenal,” Liu said.

Making it personal

The Alexa experience in a car should also be as personal as the interactions that customers have with Alexa in their homes, noted Tathavadekar. That’s possible because cars with built-in Alexa are just another endpoint for individual Alexa accounts, which are stored in the cloud.

“We are able to render experiences on all the endpoints where that particular person might be operating,” he said. “You might create a playlist at home and play it in your car.”

In a similar fashion, a driver in their car can ask Alexa to add an item to a shopping list that they started in the kitchen or ask Alexa to lock the front door of a connected smart home.

Providing that personal and familiar experience with Alexa in cars requires a system that seamlessly extends the Alexa experience from an Echo at home to a vehicle that might be traveling down a congested highway with patchy connectivity.

“The big chunk of our work is to collaborate with the major car manufacturers to create unique experiences that delight car owners,” said Liu. “They are our partners who help deliver the product to customers. They’re making the car, and we add this piece of personalized voice assistant into the digital experience.”

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