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, WA, Seattle
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 subscriptions such as Apple TV+, HBO Max, Peacock, 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 team member, 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 As a highly experienced and seasoned science leader, you will apply state of the art natural language processing and computer vision research to video centric digital media, while also responsible for creating and maintaining the best environment for applied science in order to recruit, retain and develop top talent. You will lead the research direction for a team of deeply talented applied scientists, creating the roadmaps for forward-looking research and communicate them effectively to senior leadership. You will also hire and develop applied scientists - growing the team to meet the evolving needs of our customers. About the team This team's mission is to deeply understand all content and empower all customers with relevant language options, innovative accessibility assists, and rich title-information across all their content-experiences on Prime Video. We create and publish content on-time that's meaningful, accurate, and accessible to every customer globally. We delight our customers by pushing the boundaries of content understanding and enrichment. Through inclusion and innovation, we do the most fulfilling work of our career.
GB, MLN, Edinburgh
Do you want to make a real difference to real people's lives? Want to design and build fair and explainable systems which automate recruitment processes across Amazon? Come and be part of a team that develops new machine learning (ML) technologies, which help Amazon scale for its customers by recruiting diverse teams. Join our Recommendations team within Intelligent Talent Acquisition (ITA) where you’ll build machine learning products that transform how job seekers find opportunities and recruiters discover talent. You’ll develop sophisticated recommendation systems powering both Amazon Jobs and internal hiring platforms, operating at global scale to match the right people with the right positions. Using techniques including representation learning, reinforcement learning, and probabilistic modeling, your work will directly improve efficiency for recruiters and help candidates find their ideal roles. This position offers the chance to solve complex problems with significant impact by creating systems that make Amazon’s entire hiring ecosystem more effective while collaborating with scientists across the organization. Key job responsibilities - Design and implement machine learning models that power recommendation systems for job seekers and recruiters, ensuring high performance, scalability, and reliability at global scale. Our ideal candidate has a strong scientific foundation and experience of statistical analysis and model building and has a passion for fairness and explainability in ML systems. - Collaborate with engineers, scientists, and product managers to define requirements, create solutions, and deliver products that improve the hiring experience. - Participate in the full software development lifecycle including scoping, design, coding, testing, documentation, deployment, and maintenance of recommendation systems and ML models. - Solve complex ML problems using optimal data structures and algorithms, making thoughtful trade-offs between efficiency and maintainability. - Stay current with scientific literature and develop novel approaches that address business challenges in talent acquisition. You will have the opportunity to provide feedback on scientific work across the organization helping the entire Intelligent Talent Acquisition organization improve. A day in the life You might spend the morning reviewing a colleague’s code for a new recommendation algorithm feature, then collaborate with product managers to refine requirements for an upcoming enhancement. After lunch, you’ll dive into model development, analyzing performance metrics from recent A/B tests and implementing improvements to the job-seeker recommendation pipeline. Throughout the day, you’ll participate in scientific discussions with peers across the organization, providing valuable feedback while continuing to refine your expertise. About the team The Recommendations team is a hybrid group of software engineers and applied scientists located in Edinburgh. We build tools that match people to jobs and jobs to people, optimizing experiences for both recruiters and candidates. Our work directly impacts Amazon’s ability to find and hire exceptional talent globally. The team maintains a collaborative environment with regular knowledge sharing and mentorship opportunities. We work closely with our product teams to understand business needs and develop innovative scientific solutions that improve hiring outcomes across both industry and student requisitions worldwide.
US, NY, New York
We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning algorithms for motor control systems in robots. In this role, you will focus on creating and optimizing intelligent motor control strategies to enable robots to perform complex, whole-body tasks. Your contributions will be essential in advancing robotics by enabling fluid, reliable, and safe interactions between robots and their environments. Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation learning, or other advanced AI techniques to achieve natural, robust, and adaptive motor behaviors - Collaborate with multi-disciplinary teams to integrate motor control systems with robotic hardware, ensuring alignment with real-world constraints such as actuator dynamics and energy efficiency - Use simulation and real-world testing to refine and validate control algorithms - Stay updated on advancements in robotics, AI, and control systems to apply advanced techniques to robotic motion challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you. an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.
US, WA, Redmond
We are searching for a talented candidate with expertise in orbital mechanics and spaceflight navigation, including LEO Satellite Orbit Determination. This position requires experience in simulation and analysis of spacecraft orbital mechanics and sequential orbit determination methods, including Extended Kalman Filters (EKF) and/or Unscented Kalman Filter (UKF). Strong analysis skills are required to develop engineering studies of complex large-scale dynamical systems. This position requires demonstrated expertise in computational analysis automation and tool development. Key job responsibilities - Perform spacecraft maneuver or navigation analysis in support of multi-disciplinary trades within the Amazon Leo team. - Contribute to prototype software development of flight algorithms. - Test and assess navigation software for integration into flight systems. - Assess and trouble-shoot the performance of Leo on-board GNSS hardware and software systems. - Work closely with GNC engineers to manage on-orbit performance and develop flight dynamics operations processes. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. A day in the life - Interacting with GNC teams to evaluate and troubleshoot satellite issues. - Working within the Flight Dynamics Research team to prioritize tasks. - Performing analysis, simulation, testing and documentation to address assigned tasks.
BR, SP, Sao Paulo
Are you passionate about helping customers achieve business transformation through AI? Do you want to lead forward-deployed teams that embed directly into the enterprise and unlock real business outcomes? And are you ready to operate as a general manager across engineering, science, and commercial strategy in the fastest-moving space in AI and infrastructure? The AWS Generative AI Innovation Center (GenAIIC) is on a mission to accelerate enterprise AI transformation across global customers going from beyond isolated use cases to holistic, C-suite-sponsored initiatives that reshape how organizations operate. We combine deep AI expertise across science, strategy, and business transformation. We start with the customer's most critical operational challenges and work backwards, and deploy multidisciplinary teams that embed with the customer, prove impact in 45-day sprints, and expand across the enterprise. We are a fast-moving, entrepreneurial team that values leaders who can operate across technical depth and commercial breadth. You will lead a team of ML engineers, AI scientists, and AI strategists who work alongside customers to architect and deliver AI solutions that move and stay in production, realizing value. You will regularly engage with CFOs, CIOs, and C-suite executives. You must bring equal fluency in engineering, data science, go-to-market, and customer delivery. You are ready to roll up your sleeves alongside the team, whether that means scoping an agentic AI architecture, presenting to a board, or operationalizing a repeatable delivery motion. You will partner with customers, AWS Sales, AWS service teams, AWS industry teams and AWS Professional Services delivery teams to meet the specific needs of the customer, and extend that use to other customers. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the 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. 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. 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. Mentorship & 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. 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
US, CA, Pasadena
We are seeking an Applied Scientist to join the SAF Lab. In this role, you will lead the effort in safe reinforcement learning (RL) including the development of legged locomotion algorithms that internalize safety and are deployable on physical hardware—enabling highly dynamic robots to walk, run, avoid collisions and recover from disturbances with agility and robustness. You will develop RL architectures that interface with physics-based models (for dynamic retargeting and reward shaping), internalize safety constraints in training, sim-to-real transfer and interface with safety filters at run-time. Therefore, your work will sit at the intersection of safety-critical control and learning, and you will collaborate with others in the SAF Lab and Amazon working on perception, planning, whole-body and safety-critical control. This is an opportunity to shape the foundations of safe learning on emerging platforms that will remove bottlenecks to deployment and enable these robots to safely operate around humans. Key job responsibilities • Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots). • Design, train, and deploy reinforcement learning (RL) policies for dynamic legged locomotion including walking, running, stair climbing, and fall recovery on physical robots • Develop sim-to-real transfer pipelines that produce policies robust to the reality gap, including domain randomization, system identification, and adaptive strategies • Integrate control-based methods with RL, as inputs to the RL (dynamic retargeting and control-guided rewards), in training (internalizing safety constraints in training), and as the RL feeds into safety layers and whole-body control • Develop and maintain large-scale training infrastructure for locomotion policy learning, including physics simulation environments, domain randomization and GPU parallelization • Investigate the distillation of locomotion policies, integration with whole-body control, foundation models, VLAs, world models, perception and full-stack autonomy • Evaluate policy performance rigorously through simulation benchmarks, hardware experiments, and failure-mode analysis • Publish research at top-tier robotics and ML venues and contribute to Amazon's scientific reputation in advanced robotics • Collaborate with perception and planning teams to enable terrain-aware and goal-conditioned locomotion behaviors A day in the life 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! About the team Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, mobile manipulators, and future platforms with dynamic stability. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin robots operating alongside people at Amazon's unprecedented scale.
US, WA, Seattle
Join the AWS Perimeter Protection team as a Senior Applied Scientist, where you will bring your deep ML engineering expertise to design, build, and scale AI-driven security solutions that protect AWS customers worldwide. This role is ideal for someone who has already built and shipped production ML systems at industry scale and is looking to apply that experience to high-impact security challenges. You will own the full ML lifecycle — from research and prototyping to production deployment and optimization — powering services including Web Application Firewall, DDoS Protection, Bot Management, and Infrastructure Protection. With services spanning all AWS regions and handling trillions of requests per week, you will solve complex engineering and science problems where model performance, system reliability, and low-latency inference are critical. Key job responsibilities - Design, build, and deploy production-grade ML models and systems for real-time threat detection, mitigation, and protection against evolving cyber threats at cloud scale. - Own the full ML lifecycle end-to-end — from problem formulation, data engineering, and model development through to production deployment, monitoring, and continuous improvement. - Architect and optimize ML pipelines, training infrastructure, and serving systems to meet strict latency, throughput, and reliability requirements at AWS scale. - Bridge the gap between research and production by translating novel ML approaches into robust, scalable, and maintainable systems that operate in real-time security environments. - Design and implement feature engineering workflows and large-scale data processing pipelines to support rapid experimentation and reliable model iteration. - Collaborate closely with software engineering teams to integrate ML models into distributed, low-latency security services, driving engineering decisions around model serving, infrastructure, and system design. - Analyze large-scale production data to identify patterns, anomalies, and emerging threat vectors, and translate findings into measurable improvements to detection and mitigation capabilities. - Establish and improve best practices for ML system design, model evaluation, A/B testing, and production monitoring across the team. - Mentor junior scientists and engineers, raising the bar on both scientific rigor and engineering quality.
US, WA, Seattle
The Annapurna ML team is looking for a Senior Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch, TensorFlow, and JAX. Your role will involve working closely with our custom-built Machine Learning accelerators, Inferentia and Trainium, which represent the forefront of Annapurna innovation for advanced ML capabilities, and is the underpinning of Generative AI. As a Senior Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted scientist, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required. A day in the life Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the 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. Why Amazon? 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. Inclusive Team Culture Here at Amazon, 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 (diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & 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. 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.
US, WA, Seattle
The Automated Reasoning Group in the Amazon Neuron team is looking for an Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch and JAX. Your role will involve working closely with our custom-built Machine Learning accelerator, Trainium, which represents the forefront of innovation for advanced ML capabilities, and is the underpinning of Generative AI. In this role as an Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted AWS Neuron engineer, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, theorem provers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required.
IN, KA, Bengaluru
Customer addresses, Geospatial information and Road-network play a crucial role in Amazon Logistics' Delivery Planning systems. We own exciting science problems in the areas of Address Normalization, Geocode learning, Maps learning, Time estimations including route-time, delivery-time, transit-time predictions which are key inputs in delivery planning. As part of the Science team within DNT (Last Mile), you will partner closely with other scientists and engineers in a collegial environment to develop enterprise ML solutions with a clear path to business impact. The setting also gives you an opportunity to think about a complex large-scale problem for multiple years and building increasingly sophisticated solutions year over year. In the process there will be opportunity to innovate, explore SOTA and publish the research in internal and external ML conferences. Successful candidates will have deep knowledge of competing machine learning methods for large scale predictive modelling, natural language processing, semi-supervised & graph based learning. We also look for the experience to graduate prototype models to production and the communication skills to explain complex technical approaches to the stakeholders of varied technical expertise. Here is a glimpse of the problem spaces and technologies that we deal with on a regular basis: 1. De-duping and organizing addresses into hierarchy while handling noisy, inconsistent, localized and multi-lingual user inputs. We do this at the scale of millions of customers for established (US, EU) as well as emerging geographies (IN, MX). We make use of technologies like LLMs, Weak supervision, Graph-based clustering & Entity matching. We also use additional modalities such as building outlines in maps, street view images and 3P datasets, gazetteers. 2. Build a generic ML framework which leverages relationship between places to improve delivery experience by learning precise delivery locations and propagating attributes, such as business hours and safe places. 3. (Work done in sister teams) Developing systems to consume inputs from areal imagery and optimize our maps to enable efficient delivery planning. Also building models to estimate travel time, turn costs, optimal route and defect propensities. Key job responsibilities As an Applied Scientist I, your responsibility will be to deliver on a well defined but complex business problem, explore SOTA technologies including GenAI and customize the large models as suitable for the application. Your job will be to work on a end-to-end business problem from design to experimentation and implementation. There is also an opportunity to work on open ended ML directions within the space and publish the work in prestigious ML conferences. About the team Last Mile Address Intelligence (LMAI) team owns WW charter for address and location learning solutions which are crucial for efficient Last Mile delivery planning. The team works out of Hyderabad and Bangalore offices in India. LMAI is a part of the DNT Science team, which also owns problems in the space of maps learning and travel time estimations. The rest of the DNT Science team and senior leadership of Last Mile org works out of Bellevue office.