The science behind Echo Frames

How the team behind Echo Frames delivered longer battery life and improved sound quality inside the slim form factor of a pair of eyeglasses.

When the team behind Amazon’s Echo Frames set out to improve the next generation of their product, they needed to strike a delicate balance. Customer feedback on earlier versions of the smart audio eyeglasses centered on three elements: longer battery life, more style options, and improved sound quality.

A man with a beard is seen wearing a pair of Echo Frames glasses. He is standing outside and is pictured in three-quarters view.
Echo Frames feature custom-built speech processing technology that drastically improves word recognition — key for interacting with Alexa in windy or noisy environments.

Achieving all three of those goals would be a challenge in itself; doing that inside the slim form factor of a pair of Alexa-enabled eyeglasses upped the ante.

“All three of those goals are in tension with one another,” says Adam Slaboski, senior manager of product management and product lead for Echo Frames. The easiest way to improve battery and audio would be to increase the size of the device, but that would conflict with feedback around the importance of design. Amping up bass to improve the audio experience would consume more battery, and so on.

Finding that sweet spot was a huge effort in engineering and customer understanding.
Adam Slaboski

“Finding that sweet spot was a huge effort in engineering and customer understanding," Slaboski says.

With Echo Frames (3rd Gen) and Carrera Smart Glasses with Alexa (designed in collaboration with Safilo, one of the world’s leading eyewear companies), the Smart Eyewear team met the challenge.

The smart glasses feature enhanced audio playback, with custom-built speech-processing technology that dramatically improves word recognition — key for interacting with Alexa in windy or noisy environments. The new range of frame styles come in a variety of sizes, and all come with a significant boost in battery life.

From the outside, Echo Frames still look like a pair of regular eyeglasses. “But we changed everything on the inside,” says Jean Wang, general manager and director of Smart Eyewear. “And we learned new lessons along the way.”

Here’s how Amazon engineers and product designers tackled all three customer demands.

Turning up the volume with open-ear audio

Like previous generations of Echo Frames, the current model uses open-ear audio. In addition to fitting the form factor of a pair of glasses, this allows users to maintain awareness of their surroundings while interacting with Alexa or enjoying audio entertainment.

Related content
Combining psychoacoustics, signal processing, and speaker beamforming enhances stereo audio and delivers an immersive sound experience for customers.

The open-ear audio design has been popular with users who are blind or have low vision, notes Jenai Akina, senior product manager for Echo Frames. “It’s really beneficial that it doesn’t obstruct a critical sense like hearing,” she explains. “That form factor is really helpful for daily interactions — especially when we want to be open to engage with our environment and the people around us. Open ear allows customers to maintain awareness, while providing access to a voice assistant.”

Open-ear audio brings a host of unique challenges to the engineering process. Typical headphones and earbuds block off the ear from the outside world, preventing air from escaping. That funnels more of the sound waves from the speakers into the user’s ears. With an open-ear design, sound has to travel farther, and there is less control over direction. That could lower the audio volume and reduce clarity — and importantly, audio could leak out to people standing nearby. The key is to drive the sound pressure as much as possible toward the user’s ears while minimizing the audio leakage.

By bringing people into the lab, we can simulate real environmental noise conditions like wind, background noise in a crowded restaurant, and the sound of cars on the road.
Scott Choi

In working to improve audio quality, the team continued to hone the directionality of the sound while also working to improve volume and bass. A technique called dipole speaker configuration helps to do both. In addition to a sound porthole located near the ear canal, the frames feature a second porthole that cancels unnecessary sound while amping up bass.

With input from in-house audio experts and instruments to analyze measurements like harmonic distortion, the team came up with a set of potential tuning solutions that met objective targets for audio quality. They then tested those “flavors” of tuning in the lab with several user groups.

“By bringing people into the lab, we can simulate real environmental-noise conditions like wind, background noise in a crowded restaurant, and the sound of cars on the road,” explains senior manager of audio Scott Choi. That allowed his team to understand environmental variables in a controlled setting.

With the feedback from those focus groups, the team then selected a few of the most popular tunings to push out to beta testing, where users could provide feedback on a weekly basis.

“We see how the feedback trends change with each tuning change, which gradually allows it to mature and converge into a certain tuning,” Choi says. The result is audio calibrated to maximize intelligibility and volume without leaking private conversations (or guilty-pleasure playlists).

The Echo Frame team used a rotating arch of microphones to lest leakage. This animation shows the array moving in circles around a mannequin wearing the Gen 3 prototype, creating a 3D sphere plot of audio leakage. Via this testing, the team was able to minimize leakage to the side and back.
The Echo Frame team used a rotating arch of microphones to lest leakage. The array moved in circles around a mannequin wearing the Gen 3 prototype, creating a 3D sphere plot of audio leakage. Via this testing, the team was able to minimize leakage to the side and back.

To test leakage, the audio team rigged up a rotating arch of microphones. The array moved in circles around a mannequin wearing the Gen 3 prototype, creating a 3-D sphere plot of audio leakage. Choi explains that they focused on minimizing leakage to the side and back, and ultimately, the speakers were moved much closer to the ear to help minimize leakage and improve loudness.

Leakage isn’t the only privacy consideration. The Echo Frame team also continues to innovate on protecting users from bad actors who may get hold of their smart glasses.

Related content
Amazon senior principal engineer Luu Tran is helping the Alexa team innovate by collaborating closely with scientist colleagues.

Gen 2 protected users by requiring them to authenticate their sessions using a trusted phone. Without authentication, a user can’t invoke sensitive commands like “navigate me home,” unlocking a smart lock, or making a purchase. But customers didn’t like the added friction.

Now customers who enroll in Alexa Voice ID will be able to use their vocal fingerprints for authentication to receive responses to smart-home utterances.

“We’re the first on-the-go Alexa device to use Voice ID for privacy authentication,” Slaboski says.

Boosting battery life without cramping style

Gen 3 improves continuous music playback time to six hours, versus the four hours offered by the previous generation of Echo Frames. It also bumps battery life to up to 14 hours of moderate usage spread across playback, talk time, notifications, and Alexa interactions.

Delivering the desired loudness, bass, and audio quality while optimizing for battery life was a careful balance.
Ravi Sanapala

The team couldn’t simply slap on a bigger battery without making the Echo Frames look less like normal glasses. And with sound quality high on the priority list as well, the devices were going to need as much juice as ever. The team focused on trimming power use in standby mode, ensuring that the overall battery consumption would go down without weakening the speakers when users needed them.

“Delivering the desired loudness, bass, and audio quality while optimizing for battery life was a careful balance,” says senior product manager Ravi Sanapala. “We need the battery to last throughout as much of the day as possible and for Alexa to be available whenever users need it.”

The architectural changes in speaker placement helped keep power needs low while improving audio. The team also tweaked the placement of the battery itself, distributing its capacity differently than in Gen 2. Sanapala adds that algorithmic changes were key in balancing idle-battery conservation and on-demand device usage.

“We had to collaborate with all of our cross-functional teams to optimize everything,” Sanapala says.

Gen 3 also features an all-new charging stand, which is designed for compatibility with all frame shapes and keeps lenses upright, protecting them from scratches while wirelessly charging.

Making smart eyewear look like eyewear

Making glasses that are suitable for everyday wear has always been a priority. “One of our goals has always been to develop technology that appears when you need it and disappears when you don’t,” says Wang.

Previous models of Echo Frames have come in a single, one-size-fits-all style.

A person is seen wearing Echo Frames sunglasses outside. The person carries a notebook and is looking down at it, and there are some buildings and blue sky in the background.
The Echo Frames team consulted with both internal and external eyewear designers to review common and popular styles of frames, and to survey potential customers about their preferences.

“That was a very intentional move,” Wang explains. “We wanted to start simply and learn from customer feedback.”

Gen 2’s flexible spring hinge and adjustable temple tips ensured that the single size fit many different faces. In fact, Wang says, while the goal was to fit around half of all potential users, they’ve found that 85 percent of the adult population can comfortably wear the Gen 2 design.

But with Gen 3, Wang says, the team needed to go beyond designing glasses that looked typical. Customers wanted glasses that looked stylish, too.

The team consulted with both internal and external eyewear designers to review common and popular styles of frames, as well as “edgier” designs, and to survey potential customers about their preferences. After testing options with beta customers, they settled on a variety of styles in various colors that cover a range of aesthetics. They also switched to an acetate material to match the feel of high-end eyewear.

Related content
How a team of designers, scientists, developers, and engineers worked together to create a truly unique device in Echo Show 10.

While each style will still come in a single size, the range of designs will accommodate even more faces than Gen 2, as the collection spans narrow, medium, and wide fits. Each style features adjustable temple tips constructed out of silicone around a lightweight titanium core for better fit. And despite the boost in battery life, the temples of Gen 3 frames have actually been slimmed down. Wang notes that competitive products often place large batteries behind a user’s ears. But presenting Echo Frames users with something that bulky and uncomfortable was never on the table.

“We were working with really heavy constraints,” Wang says. “So we have been very deliberate in making design choices in the service of our customer. That’s challenged us to be innovative and really push the limits of what’s possible in the architecture of our designs.”

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, CA, Palo Alto
The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!
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
We are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals.
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
GB, London
We are looking for a Senior Economist to work on exciting and challenging business problems related to Amazon Retail’s worldwide product assortment. You will build innovative solutions based on econometrics, machine learning, and experimentation. You will be part of a interdisciplinary team of economists, product managers, engineers, and scientists, and your work will influence finance and business decisions affecting Amazon’s vast product assortment globally. If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you. Key job responsibilities * Work on a challenging problem that has the potential to significantly impact Amazon’s business position * Develop econometric models and experiments to measure the customer and financial impact of Amazon’s product assortment * Collaborate with other scientists at Amazon to deliver measurable progress and change * Influence business leaders based on empirical findings
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide
US, MA, N.reading
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. As a Senior Applied Scientist in Sensing, you will develop innovative and complex sensing systems for our emerging robotic solutions and improve existing on-robot sensing to optimize performance and enhance customer experience. The ideal candidate has demonstrated experience designing and troubleshooting custom sensor systems from the ground up. They enjoy analytical problem solving and possess practical knowledge of robotic design, fabrication, assembly, and rapid prototyping. They thrive in an interdisciplinary environment and have led the development of complex sensing systems. Key job responsibilities - Design and adapt holistic on-robot sensing solutions for ambiguous problems with fluid requirements - Mentor and develop junior engineers - Work with an interdisciplinary team to execute product designs from concept to production including specification, design, prototyping, validation and testing - Own the detailed design and performance of a sensing system design - Work with the Operations, Manufacturing, Supply Chain and Quality organizations as well as vendors to ensure a smooth transition of concept to product - Write functional specifications, design verification plans, and functional test procedures - Exhibit role model behaviors of applied science best practices, thorough and predictive analysis and cradle to grave ownership About the team Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!