Echo Show 10, Charcoal, UI.jpg
A a team of designers, engineers, software developers, and scientists spent many months hypothesizing, experimenting, learning, iterating, and ultimately creating Echo Show 10, which was released Thursday.

The intersection of design and science

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

During the prototyping stages of the journey that brought Echo Show 10 to life, the design, engineering, and science teams behind it encountered a surprise: one of their early assumptions was proving to be wrong.

The feature that most distinguishes the current generation from its predecessors is the way the device utilizes motion to automatically face users as they move around a room and interact with Alexa. This allows users to move around in the kitchen while consulting a recipe, or to move freely when engaging in a video call, with the screen staying in view.

Naturally, or so the team thought, users would want the device to remain facing them, matching where they were at all times. “You walk from the sink to the fridge, say, while you're using the device for a recipe, the device moves with you,” David Rowell, principal UX designer said. Because no hardware existed, the team had to create a method of prototyping, so they turned to virtual reality (VR). That approach enabled Echo Show 10 teams to work together to test assumptions — including their assumption about how the screen should behave. In this case, what they experienced in VR made them change course.

Echo Show 10 animation

“We had a paradigm that we thought worked really well, but once we tested it, we quickly discovered that we don't want to be one-to-one accurate,” said David Jara, senior UX motion designer. In fact, he said, the feedback led them to a somewhat unexpected conclusion: the device should actually lag behind the user. “Even though, from a pragmatic standpoint, you would think, ‘Well, this thing is too slow. Why can't it keep up?’, once you experienced it, the slowed down version was so much more pleasant.”

This was just one instance of a class of feedback and assumption-changing research that required a team of designers, engineers, software developers, and scientists to constantly iterate and adapt. Those teams spent many months hypothesizing, experimenting, learning, iterating, and ultimately creating Echo Show 10, which was released Thursday. Amazon Science talked to some of those team members to find out how they collaborated to tackle the challenges of developing a motorized smart display and device that pairs sound localization technology and computer vision models.

From idea to iteration

“The idea came from the product team about ways we could differentiate Echo Show,” Rowell said. “The idea came up about this rotating device, but we didn't really know what we wanted to use it for, which is when design came in and started creating use cases for how we could take advantage of motion.”

The design team envisioned a device that moved with users in a way that was both smooth and provided utility.

Adding motion to Echo Show was a really big undertaking. There were a lot of challenges, including how do we make sure that the experience is natural.
Dinesh Nair, applied science manager

That presented some significant challenges for the scientists involved in the project. “Adding motion to Echo Show was a really big undertaking,” said Dinesh Nair, an applied science manager in Emerging Devices. “There were a lot of challenges, including how do we make sure that the experience is natural, and not perceived as creepy by the user.”

Not only did the team have to account for creating a motion experience that felt natural, they had to do it all on a relatively small device. "Building state-of-the-art computer vision algorithms that were processed locally on the device was the greatest challenge we faced," said Varsha Hedau, applied science manager.

The multi-faceted nature of the project also prompted the teams to test the device in a fairly new way. “When the project came along, we decided that that VR would be a great way to actually demonstrate Echo Show 10, particularly with motion,” Rowell noted. “How could it move with you? How does it frame you? How do we fine tune all the ways we want machine learning to move with the correct person?”

Behind each of those questions lay challenges for the design, science, and engineering teams. To identify and address those challenges, the far-flung teams collaborated regularly, even in the midst of a pandemic. “It was interesting because we’re spread over many different locations in the US,” Rowell said. “We had a lot of video calls and VR meant teams could very quickly iterate. There was a lot of sharing and VR was great for that.”

Clearing the hurdles

One of the first hurdles the teams had to clear was how to accurately and consistently locate a person.

“The way we initially thought about doing this was to use spatial cues from your voice to estimate where you are,” Nair said. “Using the direction given by Echo’s chosen beam, the idea was to move the device to face you, and then computer vision algorithms would kick in.”

The science behind Echo Show 10

A combination of audio and visual signals guide the device’s movement, so the screen is always in view. Learn more about the science that empowers that intelligent motion.

That approach presented dual challenges. Current Echo devices form beams in multiple directions and then choose the best beam for speech recognition. “One of the issues with beam selection is that the accuracy is plus or minus 30 degrees for our traditional Echo devices,” Nair observed. “Another is issues with interference noise and sound reflections, for example if you place the device in a corner or there is noise near the person.” The acoustic reflections were particularly vexing since they interfere with the direct sound from the person speaking, especially when the device is playing music. Traditional sound source localization algorithms are also susceptible to these problems.

The Audio Technology team addressed these challenges to determine the direction of sound by developing a new sound localization algorithm. “By breaking down sound waves into their fundamental components and training a model to detect the direct sound, we can accurately determine the direction that sound is coming from,” said Phil Hilmes, director of audio technology. That, along with other algorithm developments, led the team to deliver a sound direction algorithm that was more robust to reflections and interference from noise or music playback, even when it is louder than the person’s voice.

Rowell said, “When we originally conceived of the device, we envisioned it being placed in open space, like a kitchen island so you could use the device effectively from multiple rooms.” Customer feedback during beta testing showed this assumption ran into literal walls. “We found that people actually put the device closer to walls so the device had to work well in these positions.” In some of these more challenging positions, using only audio to find the direction is still insufficient for accurate localization and extra clues from other sensors are needed.

Echo Show 10, Charcoal, Living room.jpg
Echo Show 10 designers initially thought it would be placed in open space, like a kitchen island. Feedback during beta testing showed customers placed it closer to walls, so the teams adjusted.

The design team worked with the science teams so the device relied not just on sound, but also on computer vision. Computer vision algorithms allow the device to locate humans within its field of view, helping it improve accuracy and distinguish people from sounds reflecting off walls, or coming from other sources. The teams also developed fusion algorithms for combining computer vision and sound direction into a model that optimized the final movement.

That collaboration enabled the design team to work with the device engineers to limit the device’s rotation. “That approach prevented the device from turning and basically looking away from you or looking at the wall or never looking at you straight on,” Rowell said. “It really tuned in the algorithms and got better at working out where you were.”

The teams undertook a thorough review of every assumption made in the design phase and adapted based on actual customer interactions. That included the realization that the device’s tracking speed didn’t need to be slow so much as it needed to be intelligent.

“The biggest challenge with Echo Show 10 was to make motion work intelligently,” said Meeta Mishra, principal technical program manager for Echo Devices. “The science behind the device movement is based on fusion of various inputs like sound source, user presence, device placement, and lighting conditions, to name a few. The internal dog-fooding, coupled with the work from home situation, brought forward the real user environment for our testing and iterations. This gave us wider exposure of varied home conditions needed to formulate the right user experience that will work in typical households and also strengthened our science models to make this device a delight.”

Frame rates and bounding boxes

Responding to the user feedback about the preference for intelligent motion meant the science and design teams also had to navigate issues around detection. “Video calls often run at 24 frames a second,” Nair observed. “But a deep learning network that accurately detects where you are, those don't run as fast, they’re typically running at 10 frames per second on the device.”

That latency meant several teams had to find a way to bridge the difference between the frame rates. “We had to work with not just the design team, but also the team that worked on the framing software,” Nair said. “We had to figure out how we could give intermediate results between detections by tracking the person.”

By breaking down sound waves into their fundamental components and training a model ... we can accurately determine the direction that sound is coming from.
Phil Hilmes, director of audio technology

Hedau and her team helped deliver the answer in the form of bounding boxes and Kalman filtering, an algorithm that provides estimates of some unknown variables given the measurements observed over time. That approach allows the device to, essentially, make informed guesses about a user’s movement.

During testing, the teams also discovered that the device would need to account for the manner in which a person interacted with it. “We found that when people are on a call, there are two use cases,” Rowell observed. “They're either are very engaged with the call, where they’re close to the device and looking at the device and the other person on the other end, or they're multitasking.”

The solution was born, yet again, from collaboration. “We went through a lot of experiments to model which user experience really works the best,” Hedau said. Those experiments resulted in utilizing the device’s CV to determine the distance between a person and Echo Show 10.

“We have settings based on the distance that the customer is from the device, which is a way to roughly measure how engaged a customer is,” Rowell said. “When a person is really up close, we don't want the device to move too much because the screen just feels like it's fidgety. But if somebody is on a call and multitasking, they're moving a lot. In this instance, we want smoother transitions.”

Looking to the future

The teams behind the Echo Show 10 are, unsurprisingly, already pondering what’s next. Rowell suggested that, in the future, the Echo Show might show a bit of personality. "We can make the device more playful," Rowell said. "We could start to express a lot of personality with the hardware." [Editor’s note: Some of this is currently enabled via APIs; certain games can “take on new personality through the ability to make the device shake in concert with sound effects and on-screen animations.”]

Nair said his team will also focus on making the on-device processing even faster. “A significant portion of the overall on-device processing is CV and deep learning,” he noted. “Deep networks are always evolving, and we will keep pushing that frontier.”

“Our teams are working continuously to further push the performance of our deep learning models in corner cases such a multi-people, low lighting, fast motions, and more,” added Hedau.

Whatever route Echo Show goes next, the teams behind it already know one thing for certain: they can collaborate their way through just about anything. “With Echo Show 10, there were a lot of assumptions we had when we started, but we didn’t know which would prove true until we got there,” Jara said. “We were kind of building the plane as we were flying it.”


View from space of a connected network around planet Earth representing the Internet of Things.
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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
US, CA, Milpitas
We are a passionate team of doers that apply cutting-edge advances in technology to solve real-world problems and transform our customers’ experiences in ways we can’t even imagine yet. As an Applied Scientist, you will be working with a unique and gifted team developing exciting products for customers and collaborating with cross-functional teams.Responsibilities· Collaborate across functions to , develop and implement algorithms to solve high-impact problems· Evaluate statistical modeling and Machine Learning approaches using historical data· Define requirements and measurement criteria for scientific and machine learning models.· Translate model prototypes into secure, stable, testable, and maintainable production services.· Develop automated approaches towards monitoring model performance and evaluating impact.· Encourage and support knowledge-sharing within team and external groups· Responsible for influencing technical decisions in areas of /modelling that you identify as critical future offerings· Deliver algorithm and ML projects from beginning to end, including understanding the customer needs, aggregating data, exploring data, building & validating predictive models, and deploying completed models.Amazon is looking for an Applied Scientist to join an exciting new project team working to build a completely new, best in class . Our team is fast paced, highly collaborative and is organized like a startup.Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.Our main office is Milpitas Ca but other US-based Amazon centers are ok.
US, VA, Arlington
Are you seeking an environment where you can drive innovation? Do you want to apply machine learning techniques and advanced statistical modeling to solve real world problems to help Amazon reach and delight millions of customers around the world? Do you want to play a crucial role in the future of Amazon?Every time an Amazon customer makes an order they leverage the most advanced and sophisticated supply chain the world has ever seen, Amazon, and its global network comprising of cutting edge software, fulfillment centers, sort centers, delivery stations, airports, customer service centers, physical stores, robots, airplanes, trucks, vans, trucks, world class employees, trusted partners, and more. Conducting a symphony of this scale comes with significant costs.Here in WW Consumer Finance, our data scientists raise the bar on our ability to fulfill promises to customers at greater convenience, speed, and value through:· Developing visual solutions which help Amazonians search, find, compare, and buy goods / services critical to Amazon's operations· Developing production-ready machine learning solutions to drive savings across Amazon's corporate procurement catalog· Working with technical and non-technical customers across every step of data science project life cycle.· Collaborating with our dedicated product, data engineering, and software development teams to create production implementations for large-scale data analysis.· Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business.· Presenting research results to our internal research community.· Leading training and informational sessions on our science and capabilities.Your contributions will be seen and recognized broadly within Amazon, potentially contributing to the Amazon research corpus and patent portfolio.
US, VA, Arlington
Are you seeking an environment where you can drive innovation? Do you want to apply machine learning techniques and advanced statistical modeling to solve real world problems to help Amazon reach and delight millions of customers around the world? Do you want to play a crucial role in the future of Amazon?Every time an Amazon customer makes an order they leverage the most advanced and sophisticated supply chain the world has ever seen, Amazon, and its global network comprising of cutting edge software, fulfillment centers, sort centers, delivery stations, airports, customer service centers, physical stores, robots, airplanes, trucks, vans, trucks, world class employees, trusted partners, and more. Conducting a symphony of this scale comes with significant costs.Here in WW Consumer Finance, our quantitative researchers raise the bar on our ability to fulfill promises to customers at greater convenience, speed, and value through:· Developing models to support the identification of investment opportunities consistent with Amazon strategic priorities· Developing models identifying synergy opportunities and risks in potential transactions· Serving as a subject matter expert on investment lead pipeline and valuation methodologies· Establish the ongoing processes, skill sets, and strategy that will enable Amazon to continue to build out our financial engineering competency, in the face of extremely fast growth and a rapidly changing industry· Working with technical and non-technical customers across every step of data science project life cycle.· Collaborating with our dedicated product, data engineering, and software development teams to create production implementations for large-scale data analysis.· Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business.· Presenting research results to our internal research community.· Leading training and informational sessions on our science and capabilities.Your contributions will be seen and recognized broadly within Amazon, potentially contributing to the Amazon research corpus and patent portfolio.
US, VA, Arlington
Are you seeking an environment where you can drive innovation? Do you want to apply machine learning techniques and advanced statistical modeling to solve real world problems to help Amazon reach and delight millions of customers around the world? Do you want to play a crucial role in the future of Amazon?Every time an Amazon customer makes an order they leverage the most advanced and sophisticated supply chain the world has ever seen, Amazon, and its global network comprising of cutting edge software, fulfillment centers, sort centers, delivery stations, airports, customer service centers, physical stores, robots, airplanes, trucks, vans, trucks, world class employees, trusted partners, and more. Conducting a symphony of this scale comes with significant costs.Here in WW Consumer Finance, our applied scientists raise the bar on our ability to fulfill promises to customers at greater convenience, speed, and value through:· Developing production ready solutions which help Amazonians search, find, compare, and buy goods / services critical to Amazon's operations· Developing production-ready machine learning solutions to improve Amazon's corporate procurement catalog· Working with technical and non-technical customers across every step of data science project life cycle.· Collaborating with our dedicated product, data engineering, and software development teams to create production implementations for large-scale data analysis.· Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business.· Presenting research results to our internal research community.· Leading training and informational sessions on our science and capabilities.Your contributions will be seen and recognized broadly within Amazon, potentially contributing to the Amazon research corpus and patent portfolio.
US, VA, Arlington
Are you seeking an environment where you can drive innovation? Do you want to apply machine learning techniques and advanced statistical modeling to solve real world problems to help Amazon reach and delight millions of customers around the world? Do you want to play a crucial role in the future of Amazon?Every time an Amazon customer makes an order they leverage the most advanced and sophisticated supply chain the world has ever seen, Amazon, and its global network comprising of cutting edge software, fulfillment centers, sort centers, delivery stations, airports, customer service centers, physical stores, robots, airplanes, trucks, vans, trucks, world class employees, trusted partners, and more. Conducting a symphony of this scale comes with significant costs.Here in WW Consumer Finance our applied scientists raise the bar on our ability to fulfill promises to customers at greater convenience, speed, and value through:· Developing production ready solutions which help Amazonians search, find, compare, and buy goods / services critical to Amazon's operations· Developing production-ready machine learning solutions to improve Amazon's corporate procurement catalog· Working with technical and non-technical customers across every step of data science project life cycle.· Collaborating with our dedicated product, data engineering, and software development teams to create production implementations for large-scale data analysis.· Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business.· Presenting research results to our internal research community.· Leading training and informational sessions on our science and capabilities.Your contributions will be seen and recognized broadly within Amazon, potentially contributing to the Amazon research corpus and patent portfolio.
US, WA, Seattle
We are constantly making Alexa the best voice assistant in the world. Amazon’s Alexa cloud service and Echo devices are used every day, by people you know, in and about their homes. The Alexa Monetization team is hiring talented and experienced Sr. Applied Scientists to help building the next generation products for Alexa across multiple channels and domains. We are seeking an experienced, entrepreneurial, big thinker for a confidential new initiative within Alexa. You will be joining a team doing innovative work, making a direct impact to customers, showing measurable success, and building with the latest natural language processing systems. If you are holding out for an opportunity to:Make a huge impact as an individual· Be part of a team of smart and passionate professionals who will challenge you to grow every day· Solve difficult challenges using your expertise in coding elegant and practical solutions· Create applications at a massive scale used by millions of people· Work with machine learning systems to deliver real experiences, not just researchAnd you are experienced with…· Drive applied science (machine learning) projects end-to-end ~ from ideation, analysis, prototyping, development, metrics, and monitoring· Conduct deep analyses on massive user and contextual data sets· Propose viable modeling ideas to advance optimization or efficiency, with supporting argument, data, or, preferably, preliminary results· Design, develop, and maintain scalable, Machine Learning models with automated training, validation, monitoring and reporting· Stay familiar with the field and apply state-of-the-art Machine Learning techniques to NLP and related optimization problems· Produce peer-reviewed scientific paper in top journals and conferencesAnd you constantly look for opportunities to…· Innovate, simplify, reduce waste, and increase efficiencies· Use data to make decisions and validate assumptions· Automate processes otherwise performed by humans· Learn from others and help grow those around you...then we would love to chat!In 2021, we have the opportunity to build new products and features from the ground up and we are looking for strong, bias for action engineering leaders who are not afraid of taking bold bets and trying new things to improve customer experience for Alexa.As part of a new and growing team, you will be iterating on new features and products to help drive innovation and expansion. You will work on cross-functional and cross-domain opportunities; tackle challenging projects aim to accelerate experimentations in Alexa; and build out operating mechanisms and technology to enable novel customer experiences. You will be instrumental in setting the team culture, quality bar, engineering best practices, and norms. Mentoring and growing the team around you will be one of the primary ways you measure your own success. You will have the opportunity to contribute and develop deep expertise in the areas of distributed systems, machine learning, conversational technologies, user interfaces (including voice and natural user interfaces), data storage and data pipelines.This role is exciting for scientists who love to apply startup mindset to their day-to-day, enjoy working cross-functionally to master both business and technology knowledge, and are passionate about building engineering best practices. If you are looking for opportunity to learn, grow and lead, this is the position for you.