AmazonScience_EchoBuds_01.jpg
In the second-generation Echo Buds, Amazon engineers were able to produce a device that is 21 percent smaller than the first version, while maintaining costs, through a multitude of innovations and integration of components.

How the second-gen Echo Buds got smaller and better

Take a behind-the-scenes look at the unique challenges the engineering teams faced, and how they used scientific research to drive fundamental innovation.

Notebook computers, tablets, and smartphones get the tech headlines, but these are largely mature products at this point. Smaller, more personal devices are going through a torrent of iteration and innovation.

Bluetooth wireless headphones are a highly competitive category, with products with bare-bones features available for less than $50, and feature-packed devices available at prices ranging all the way up to $400.

The first Amazon Echo Buds appeared in 2019, and, the follow-up second-gen Echo Buds in April 2021. The team at Amazon improved the second-gen earbuds in almost every way. This is a behind-the-scenes look at the unique challenges the engineering teams faced in creating the latest generation Echo Buds, and how they used scientific research to drive fundamental innovation to overcome those challenges.

Ultimately, Amazon’s team was able to a deliver feature-rich product that competes with products at the high end of the price range for $120.

Atif Noori was the principal product manager for both generations of Echo Buds. Reflecting Amazon’s customer focus, he said the process of designing the latest Echo Buds began with understanding the desires of the customer.

“We work backwards from the customers and build out a set of product requirements. From there we work across multiple talented teams to deliver a lovable product,” he said.

What customers want is great audio, a comfortable fit, long battery life, and excellent connectivity with their smartphones. Of course, many of these are in tension with one another. At the high end of the hearables category, customers also want advanced features like noise cancellation and cloud-based voice services like Alexa.

Echo Buds, Glacier White, Outside.jpg
For the second-generation Echo Buds, engineers worked to redesign the main Bluetooth chip and the audio co-processor in such a way that those two components could perform the tasks of five different components in the first-generation device.

Given this catalog of customer expectations, the nugget-sized wireless earbuds are giants of engineering challenges.

Reducing size to improve comfort and fit, while still maintaining connectivity performance, staying under comfortable temperatures limits, and meeting the customer’s battery life expectations with more features, was a challenge, but one the engineering team said they were excited to tackle.   

Milos Jorgovanovic, principal system architect at Amazon Lab 126, says size and cost are the constant constraints. The Amazon engineers were able to produce a device that is 21 percent smaller than the first version Echo Buds, while maintaining costs, through a multitude of innovations and integration of components.

This began with the processors, or, to use the engineers’ lingo, the silicon, which are the heart of the device. To make the device smaller, the engineers needed to reduce the size of the battery. Easily enough done on its own, except the team also needed to do this without reducing the device's battery life.

"And really for that, the key piece is the power consumption of the silicon platform itself," Jorgovanovic said. "At the same time, we are basically trying to offer high-end features at a much lower power consumption and lower cost."

For the second-generation Echo Buds, team worked with manufacturers to redesign the main Bluetooth chip and the audio co-processor in such a way that those two components could perform the tasks of five different components found in the first-generation device.

"We basically cut the power consumption for audio and Alexa processing by at least a factor of two from what it was before," Jorgovanovic said.

We basically cut the power consumption for audio and Alexa processing by at least a factor of two from what it was before.
Milos Jorgovanovic

This was done while simultaneously improving the Alexa’s ability to hear customers speak.

Amazon started the voice category with the original Echo and Alexa launch in November 2014, so it makes sense that the latest Echo Buds would offer seamless Alexa functionality. With Alexa, a user can not only play music and make phone calls, but also set reminders, request information, and in certain cities, plan public transportation routes and get information on the train or bus they're hoping to catch, all while leaving their phone in their pocket.

"If a customer wants to take Alexa on the go, they can do that and have the same experience as they do with an Echo in their home," Noori said. "It's even more than that though. The responses are tailored for when you're on the go. For example, you can ask Alexa to remind you to buy tahini when you arrive at Whole Foods. And in some stores, you can then ask Alexa if tahini is in stock, or ask which aisle the tahini is on, which is pretty awesome.”

Achieving all of that requires not only integration with the cloud, but also a good bit of on-device processing. Jorgovanovic said improvements in the new processor allowed this to be done with less power consumption.

"We put a better digital signal processor in there, but the second, and more important piece, is that this chip was designed so that it allows very aggressive frequency and voltage scaling," he said. "What it means is that if the device is basically sitting in the air, doing very little processing, we are able to lower both the frequency and the voltage on that chip and have the chip consume much less energy."

If the user speaks and, for example, asks a friend, "Hey, Jason, how are you doing?" the device will run a small amount of processing to determine if the user said "Alexa." If the user did say "Alexa," the digital signal processor (DSP) is boosted even further, increasing the voltage, boosting the frequency, and engaging in more complex compute. At that point the device is processing the Alexa event — the information is sent to the cloud and then the response is played when it is received.

"We basically have these levels of processing, and we set the frequency and the voltage on the processor to the adequate level for the amount of processing we need. This is one of the two main things that we've done in gen two to scale down the power consumption,” Jorgovanovic explained. “The second big thing was integrating more functionality into the main Bluetooth SoC [system on a chip] by innovating on the Bluetooth protocol between the two earbuds, which reduced the number of components and power spent on interconnect. Overall, we reduced total device power by more than 35 percent relative to the first Echo Buds, and specific to that DSP processing for audio and Alexa, by at least a factor of two, if not more. And that's just, wow."

Reducing the power of the processors brought another benefit: reduced heat. "Because we pack in so much, we have to factor in heat dissipation," Noori said.  "It was not like you can add on cooling fins or a big heat sink. It required careful simulation and design."

Beyond the silicon, another major constraint in size and cost are the antennas.

Connectivity is a hurdle in wireless Bluetooth headphones because they are partially hidden in the ear. And while ears can block frequencies, the human body is also effective at blocking signals. The user's smartphone needs to connect with one of the earbuds, and then the two earbuds need to send packets to each other, with as little latency as possible.

"That's really important — the synchronization of the two ear buds — because our hearing is very sensitive to this," Jorgovanovic said. "Something like a hundred microseconds of delta between left and right can easily be felt. And, the effect is the user will sense that the audio is not coming from straight ahead, but instead coming from one side or the other."

Balamurugan Shanmugam, senior antenna design engineer, says the connectivity issues are a challenge for all wearable devices.

"This is an inherent physics problem, right? I mean, this is not unique to Amazon. Anyone working on body-worn devices or even looking just at medical devices such as wireless-enabled pacemakers will encounter the same problem," he said.

Shanmugam's challenge was to improve connectivity in a smaller package. His team's first go at the problem developed a solution, but the manufacturing costs were too high. It was time to develop a novel solution.

Just as the engineers were able to reduce the number of processors in the device, they also were able to integrate functions to accommodate a new antenna. The best location for the antenna is in the front center of the device, but that is also where a user expects to tap or use gestures. On the first-generation Echo Bud, the touch sensor and electrostatic discharge (ESD) circuits were utilizing the location an antenna needs to maximize wireless performance. To address that, the engineers invented an integrated antenna design that combines the antenna, touch, and ESD subsystems.

"The newest Echo Bud has integrated antenna, touch, and electrostatic discharge to optimize wireless performance," Shanmugam said.

Noori said that connectivity is among the features that stand out in the latest Echo Buds. "Connectivity is very solid on these devices; I'm definitely proud of the connectivity performance. I think we nailed that."

And there’s more to come.

"I think there's a lot of interesting things that can be done with earbuds that are outside of basic music playback," Noori said. "We’re continuing to innovate on behalf of our customers, and pushing out software updates. Echo Buds will continue to get better and smarter over time."

Get them in black or white with a wired charing case for $119.99 or with a wireless charging case for $139.99.

Research areas

Related content

US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
GB, London
As a STRUC Economist Intern, you'll specialize in structural econometric analysis to estimate fundamental preferences and strategic effects in complex business environments. Your responsibilities include: Analyze large-scale datasets using structural econometric techniques to solve complex business challenges Applying discrete choice models and methods, including logistic regression family models (such as BLP, nested logit) and models with alternative distributional assumptions Utilizing advanced structural methods including dynamic models of customer or firm decisions over time, applied game theory (entry and exit of firms), auction models, and labor market models Building datasets and performing data analysis at scale Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations Tackling diverse challenges including pricing analysis, competition modeling, strategic behavior estimation, contract design, and marketing strategy optimization Helping business partners formalize and estimate business objectives to drive optimal decision-making and customer value Build and refine comprehensive datasets for in-depth structural economic analysis Present complex analytical findings to business leaders and stakeholders
US, WA, Seattle
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 ensure uniqueness and consistency of product identity and 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 Key job responsibilities include: * 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 * Design and execute model distillation strategies—distilling large frontier LLMs and VLMs into compact, production-grade models—that preserve multimodal reasoning capability while dramatically reducing serving latency, cost, and infrastructure footprint at billion-product catalog scale * 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