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

IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.
IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities - Develop ML models for various recommendation & search systems using deep learning, online learning, and optimization methods - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.
US, WA, Seattle
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. The Data Center Field Engineering Team is the engineering owner for the lifecycle of AWS data center mechanical and electrical infrastructure. This includes supporting new designs and innovations through data center end-of-life, with a focus on root cause analysis of failures, capacity and availability improvement, and optimization of the existing fleet. As a Senior Data Scientist on the Field Engineering Portfolio team, you will bring advanced analytical and machine learning capabilities to one of the most critical infrastructure organizations at AWS. You will develop scalable models and data-driven frameworks that measure, predict, and improve fleet performance — including data center availability, operational efficiency, and key performance indicators (KPIs) across the global AWS data center fleet. You are an exceptionally strong communicator, both written and verbally, capable of translating complex quantitative findings into clear recommendations for senior engineering and business leadership. You will work cross-functionally with Field Engineers, Operations, Commissioning, and Construction teams to ensure that data science solutions are grounded in operational reality and drive measurable impact. You will partner with engineering teams and program managers to define metrics, identify performance gaps, and build the analytical infrastructure needed to support strategic decisions at hyper-scale. You must be adept at operating in ambiguous, fast-moving environments where speed of insight can matter as much as analytical precision. The ideal candidate brings strong problem-solving skills, stakeholder communication skills, and the ability to balance technical rigor with delivery speed and customer impact. You will develop scalable analytical approaches to evaluate performance across the data center fleet to identify regional and site-specific insights, design and run experiments, and shape our development roadmap. You will build cross-functional support within the Data Center Community to assess business problems, define metrics, and support iterative scientific solutions that balance short-term delivery with long-term science roadmaps. Key job responsibilities • Develop and maintain scalable models and analytical frameworks to measure and predict data center fleet performance, including availability, efficiency, and reliability KPIs across the global AWS infrastructure portfolio. • Apply advanced statistical and machine learning techniques to extract actionable insights from complex, large-scale operational datasets generated by data center systems (power, cooling, controls, etc.). • Partner with Field Engineers, Operations, and Portfolio Managers to identify high-impact opportunities for capacity and availability improvement, translating engineering domain knowledge into quantitative problem formulations. • Design and implement end-to-end data science workflows — from data acquisition and cleaning through model development, validation, and production deployment — enabling repeatable, scalable analysis. • Formalize assumptions about how data center systems are expected to perform and develop methods to systematically identify deviations, root causes, and high-ROI improvement opportunities. • Build self-service datasets, dashboards, and reporting mechanisms that provide Field Engineering leadership with real-time visibility into fleet health and portfolio performance. • Prepare narratives and data-driven recommendations for executive leadership that articulate decision points relative to fleet investment, risk trade-offs, and strategic priorities. • Collaborate with applied science, software engineering, and data engineering teams to ensure models integrate seamlessly with upstream and downstream systems. About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities Develop foundation models for content understanding using state-of-the-art deep learning and multimodal learning techniques to analyze video, audio, and text. Build time sequence foundation models to understand and predict customer behavior patterns and viewing trajectories. Work closely with engineers and product managers to design, implement and launch solutions end-to-end across various Prime Video experiences. Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses. Effectively communicate technical and non-technical ideas with teammates and stakeholders. Stay up-to-date with advancements and the latest modeling techniques in foundation models, multimodal learning, and time series analysis. Publish your research findings in top conferences and journals. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Science Manager to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will lead a strong science team and work closely with other science and engineering leaders, product and business partners together to build the best personalized customer experience for Prime Video. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Lead to develop AI solutions for various Prime Video recommendation and personalization systems using Deep learning, GenAI, Reinforcement Learning, recommendation system and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Hire and grow a science team working in this exciting video personalization domain. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various devices. We work closely with the engineering teams to launch our solutions in production.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities - Develop ML models for various recommendation & search systems using deep learning, online learning, and optimization methods - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Publish your research findings in top conferences and journals. About the team Prime Video Search Science team owns science solution to power search experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering teams to launch our solutions in production.
US, CA, Culver City
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. We are seeking a highly skilled and analytical Research Scientist. You will play an integral part in the measurement and optimization of Amazon Music marketing activities. You will have the opportunity to work with a rich marketing dataset together with the marketing managers. This role will focus on developing and implementing causal models and randomized controlled trials to assess marketing effectiveness and inform strategic decision-making. This role is suitable for candidates with strong background in causal inference, statistical analysis, and data-driven problem-solving, with the ability to translate complex data into actionable insights. As a key member of our team, you will work closely with cross-functional partners to optimize marketing strategies and drive business growth. Key job responsibilities Develop Causal Models Design, build, and validate causal models to evaluate the impact of marketing campaigns and initiatives. Leverage advanced statistical methods to identify and quantify causal relationships. Conduct Randomized Controlled Trials Design and implement randomized controlled trials (RCTs) to rigorously test the effectiveness of marketing strategies. Ensure robust experimental design and proper execution to derive credible insights. Statistical Analysis and Inference Perform complex statistical analyses to interpret data from experiments and observational studies. Use statistical software and programming languages to analyze large datasets and extract meaningful patterns. Data-Driven Decision Making Collaborate with marketing teams to provide data-driven recommendations that enhance campaign performance and ROI. Present findings and insights to stakeholders in a clear and actionable manner. Collaborative Problem Solving Work closely with cross-functional teams, including marketing, product, and engineering, to identify key business questions and develop analytical solutions. Foster a culture of data-informed decision-making across the organization. Stay Current with Industry Trends Keep abreast of the latest developments in data science, causal inference, and marketing analytics. Apply new methodologies and technologies to improve the accuracy and efficiency of marketing measurement. Documentation and Reporting Maintain comprehensive documentation of models, experiments, and analytical processes. Prepare reports and presentations that effectively communicate complex analyses to non-technical audiences.
US, WA, Seattle
About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading 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. About our team The Gnome team within the Sponsored Products and Brands (SPB) improves ad selection helping shoppers reach their shopping mission. To do this, we apply a broad range of machine learning, causal inference, reinforcement learning based optimization techniques and LLMs to continuously explore, learn, and optimize ads shown. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the ads experience by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. A day in the life As an Applied Scientist on this team, you will be responsible to improve quality of ads shown using in-session and offline signals via online experimentation, ML modeling, simulation, and online feedback. As an Applied Scientist on this team, you will identify opportunities for the team to make a direct impact on customers and the search experience. You will work closely with with search and retail partner teams, software engineers and product managers to build scalable real-time ML solutions. You will have the opportunity to design, run, and analyze A/B experiments that improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact while broadening your technical skillset. #GenAI