How Amazon Chime's noise cancellation works

Combining classic signal processing with deep learning makes method efficient enough to run on a phone.

PercepNet is one of the core technologies of Amazon Chime's Voice Focus feature. It is designed to suppress noise and reverberation in the speech signal, in real time, without using too many CPU cycles. This makes it usable in cellphones and other power-constrained devices. 

At Interspeech 2020, PercepNet finished second in its category (real-time processing) in the Deep Noise Suppression Challenge, despite using only 4% of a CPU core, while another Amazon Chime algorithm, PoCoNet, finished first in the offline-processing category. In this post, we'll look into the principles that make PercepNet work. For more details, you can also refer to our Interspeech paper.

Despite operating in real time, with low complexity, PercepNet can still provide state-of-the-art speech enhancement. Like most recent speech enhancement algorithms, PercepNet uses deep learning, but it applies it in a different way. Rather than have a deep neural network (DNN) do all the work, PercepNet tries to have it do as little work as possible.

Speech enhancement and STFT

Before getting into any deep learning, let's look at the job we'll be asking our machine learning model to perform. Let's consider a simple synthetic example. We start from the clean speech sample below:

We then add some non-stationary car noise on top of it:

The goal here is to take the noisy audio and make it sound as good as possible — ideally, close to the original clean audio. The standard way to represent the problem — both pre-deep learning and post-deep learning — is to use the short-time Fourier transform (STFT).

That means chopping up the signal into overlapping windows and computing the frequency content for each window. For each window of N samples (N discrete measurements of the signal amplitude), we obtain N/2 spectral magnitudes, along with their associated phases. We will refer to each output point as a frequency bin. Let's see what the magnitude of the STFT looks like for our clean signal (top) and noisy signal (bottom).

percepnet_spectrograms.jpg
The spectrograms above show the frequency content of an audio clip. The horizontal axis is time, the vertical axis is frequency, and the color represents the amount of energy at a particular time, for a particular frequency, using a log scale.

From the noisy STFT, many algorithms try to estimate the clean magnitude of each frequency while retaining the phase — which is much harder to estimate — from the noisy signal. For now, let's assume we have a magic model (an oracle) that's able to do a perfect mapping from noisy spectral magnitudes to clean. This is why we started from a synthetic example, so we can compute the oracle output. Based on oracle magnitudes but using the noisy phase, we can reconstruct the speech signal:

Certainly not bad, but also far from perfect. The noise is still audible as a form of roughness in the speech. This is due to the error in the phase, which we took from the noisy signal. While the ear is essentially insensitive to the absolute phase, what we perceive here is the inconsistency of the phase across frames. In other words, the way in which the phase changes over time still does matter.

Another issue for real-time, power-constrained operation is the number of frequency bins whose amplitudes we need to estimate. Assuming we use 20-millisecond windows, the STFT bins will be spaced 50 Hz apart. If we want to enhance all frequencies up to 20 kHz (the upper limit of human hearing), then our neural network will have to estimate 400 amplitudes, which is very computationally expensive.

Where do we go from here? If we want to improve quality, then we could also estimate phase. This is the no-compromise route taken by PoCoNet, which can get around the added complexity because it’s optimized to run on a GPU. For real-time applications on power-constrained devices, however, we can't realistically expect to have a very good phase estimator.

A perceptually relevant representation

If we want good speech quality, and we want our algorithm to run in real time on a CPU without instantly draining the battery, then we need to find a way to simplify the problem. We can do that by making the following assumptions:

  1. the general shape of the speech spectrum (a.k.a. the spectral envelope) is smooth; and 
  2. we perceive it with a nonlinear frequency resolution, corresponding to the human ear’s auditory filters (a.k.a. critical bands)

In other words, (1) the speech spectrum tends not to have sharp discontinuities, and (2) the human auditory system perceives low frequencies with higher resolution than high frequencies.

We can follow both of those assumptions by representing the speech spectrum using bands spaced according to equivalent rectangular bandwidth (ERB). ERB-spaced bands divide the spectrum into bands of increasing width, capturing coarser spectral information as frequency increases, much the way the human auditory system does.

Because multiple STFT bins are assigned to each band, the spectral representation is smoother: any discontinuity in frequency is averaged out.

Nonlinearly spaced bands make our model much simpler. Instead of 400 frequency bins, we need only 34 bands. In practice, we model these bands as overlapping filters, which are most responsive to the frequencies at the centers of the bands (the tips of the triangles below) and decreasingly responsive to frequencies farther from the center (the sides of the triangles; note the 50% overlap between bands):

bands.png

For each of the bands above, we compute a gain between 0 and 1; then, all we need to do is interpolate those band gains and we're done. Now, let's listen to how this would sound — still using the oracle for band magnitudes:

Our complexity went down, but so did the quality. The roughness we noticed previously is now even more obvious and sounds a bit like heavy distortion. It's not that surprising, since we are still changing only the magnitude spectrum, but with only 34 degrees of freedom rather than 400.

So what are we missing here? The missing piece is that the ear doesn't only perceive the spectral envelope of the signal; it also perceives whether the signal is made of tones (voiced sounds), noise (unvoiced sounds), or a mix of the two. Vowels are mostly composed of tones (harmonics) at multiples of a fundamental frequency (the pitch), whereas many consonants (such as the /s/ phoneme) are mostly noise-like. 

Our enhanced speech sounds rough because the tonal vowels contain more noise than they should. To enhance our tones, we can use a time-domain technique called comb filtering. Comb filtering is often an undesired effect in which room reverberation boosts or attenuates frequencies at regular intervals. But by carefully tuning our comb filter to the pitch of the voice we're trying to enhance, we can keep all the tones and remove most of the noise. Below is an example of the frequency response of the comb filter for a pitch of 200 Hz.

pitch.png

The pitch is the period at which a periodic signal (nearly) repeats itself. Pitch estimation is a hard problem, especially in the noisy conditions we have here. To estimate the pitch, we try to match a signal with past versions of itself, finding the period T that maximizes the correlation between x(n) and x(n-T). We then use dynamic programming (the Viterbi algorithm) to find a pitch trajectory that is consistent (e.g. no large jumps) over time.

Since we often want to retain at least some of the noise, we can simply do a mix between the noisy audio and the comb-filtered audio to get exactly the tone/noise ratio we want. By doing the mixing in the frequency domain, we can control that mix on a band-by-band basis, even though the comb filter is computed in the time domain. The exact ratios (or filtering strengths) to use for the mixing can be adjusted in such a way that the ratio of tones to noise in the output is about the same as it was in the clean speech. This is what our oracle (using the optimal strengths) now sounds like with comb filtering:

There’s still a little roughness, but our quality is already better than that of our spectral-magnitude oracle, despite using far fewer parameters. It now seems that we're as close to the original properties of the speech as we could get with our model. So what else can we do to further improve quality? The answer is simple: we cheat! 

To be more specific, we can cheat the human auditory system a bit by further attenuating the frequency bands that are still too noisy. Our speech will deviate slightly from the correct spectral envelope, but the ear will not notice that too much. It will just notice the noise less. This kind of post-filtering has been used in speech codecs since the 1980s but (as far as we know) not in speech enhancement systems. Adding the post-filter to our oracle gives us the following:

We're now quite close to the perfect clean speech. At this point, our limiting factor will most certainly be the DNN model and not the representation we use. The good thing is that our DNN has to estimate only 34 band gains (between 0 and 1) and 34 comb-filtering strengths (also between 0 and 1). This is much easier than estimating 400 magnitudes/gains — and possibly also 400 phases.

Adding a DNN

So far, we’ve assumed a perfect model for predicting band gains (the oracle). In practice, we need to use a DNN. But all the work we did in the previous section was meant to make the DNN design as boring as possible.

Since we replaced our initial 400 frequency bins with just 34 bands, there's no reason to use convolutional layers across frequency. Instead, we just go with convolutional layers across time and — most importantly — recurrent layers that provide longer-term memory to the system. We found that simple gated recurrent units (GRUs) work well, but long-short-term-memory networks (LSTMs) would probably have worked as well.

dnn_model.png
DNN model

In our DNN modelf is an input feature vector that contains all the band-based spectral information we need. The outputs are the band gains b and the comb-filtering strengths b. Now all we need to do is train our network using hours of clean speech to which we add various levels of noise and reverberation. Since we have the clean speech, we can compute the optimal (oracle) gains and filtering strengths and use them as training targets. Our complete system using the trained DNN sounds like this:

Obviously, it does not sound as good as the last oracle — no enhancement DNN is perfect — but it's still a big improvement over the noisy input speech. Our Interspeech 2020 Deep Noise Suppression Challenge samples page provides some examples of how PercepNet performs in real conditions.

Using it in real time

The DNN model above contains about eight million weights. For each new window, we use each weight exactly once, which means eight million multiply-add operations per window. With 20-millisecond windows and 50% overlap, we have 100 windows per second of speech, so 800 million multiply-add operations per second. 

Thankfully, DNNs tend to be quite robust to small perturbations, so we can quantize all our weights to just eight bits with a negligible effect on perceived audio quality. Thanks to SIMD instructions on modern CPUs, this makes it possible to run our network really efficiently. On a modern laptop CPU, it takes less than 5% of one core to run PercepNet in real time.

To be useful in real-time communications applications, PercepNet should not add too much delay. The seemingly arbitrary choice of 20-millisecond windows with 50% overlap means that it consumes audio 10 milliseconds at a time. This is good because most audio codecs (including Opus, which is used in WebRTC) encode audio in 20-millisecond packets. So we can run the algorithm exactly twice per packet without the PercepNet block size causing an increase in delay. 

There are, of course, some delays we cannot avoid. The overlap between windows means that the STFT itself requires 10 milliseconds for reconstruction. On top of that, we typically allow the DNN to look two windows (20 millseconds) into the future, so it can make better decisions. This gives us a total of 30 milliseconds extra delay from the algorithm, which is acceptable in most scenarios.

If you would like to know more about the details of PercepNet, you can read our Interspeech 2020 paper. The idea behind PercepNet is quite versatile and could be applied to other problems, including acoustic echo control and beamforming post-filtering. In future posts, we will see how we can make PercepNet very efficient on CPUs and even how to run it as Web Assembly (WASM) code inside web browsers for WebRTC-based applications.

Related content

US, WA, Seattle
Job description: We are reimagining Amazon Search with an interactive conversational experience that helps you find answers to product questions, perform product comparisons, receive personalized product suggestions, and so much more, to easily find the perfect product for your needs. We’re looking for the best and brightest across Amazon to help us realize and deliver this vision to our customers right away. This will be a once in a generation transformation for Search, just like the Mosaic browser made the Internet easier to engage with three decades ago. If you missed the 90s—WWW, Mosaic, and the founding of Amazon and Google—you don’t want to miss this opportunity.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, (Bayesian) time series, macroeconomic, as well as basic familiarity with Matlab, R, or Python is necessary, and experience with SQL would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Research and implement novel machine learning and statistical approaches. Lead strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. Drive the vision and roadmap for how ML can continually improve Selling Partner experience. About the team Selling Partner Experience Science (SPeXSci) is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.
US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
RO, Iasi
Amazon’s mission is to be earth’s most customer-centric company and our team is the guardian of our customer’s privacy. Amazon SDO Privacy engineering operates in Austin – TX, US and Iasi, Bucharest – Romania. Our mission is to develop services which will enable every Amazon service operating with personal data to satisfy the privacy rights of Amazon customers. We are working backwards from the customers and world-wide privacy regulations, think long term, and propose solutions which will assure Amazon Privacy compliance. Our external customers are world-wide customers of Amazon Retail Website, Amazon B2B services (e.g. Seller central, App / Skill Developers), and Amazon Subsidiaries. Our internal customers are services within Amazon who operate with personal data, Legal Representatives, and Customer Service Agents. You can opt-in for being part of one of the existing or newly formed engineering teams who will contribute to Amazon mission to meet external customers’ privacy rights: Personal Data Classification, The Right to be forgotten, The right of access, or Digital Markets Act – The Right of Portability. The ideal candidate has a great passion for data and an insatiable desire to learn and innovate. A commitment to team work, hustle and strong communication skills (to both business and technical partners) are absolute requirements. Creating reliable, scalable, and high-performance products requires a sound understanding of the fundamentals of Computer Science and practical experience building large-scale distributed systems. Your solutions will apply to all of Amazon’s consumer and digital businesses including but not limited to Amazon.com, Alexa, Kindle, Amazon Go, Prime Video and more. Key job responsibilities As an data scientist on our team, you will apply the appropriate technologies and best practices to autonomously solve difficult problems. You'll contribute to the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. You will collaborate with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. Your work will directly impact the trust customers place in Amazon Privacy, globally.
JP, 13, Tokyo
The JP Economics team is a central science team working across a variety of topics in the JP Retail business and beyond. We work closely with JP business leaders to drive change at Amazon. We focus on solving long-term, ambiguous and challenging problems, while providing advisory support to help solve short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel economic/econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with JP- EU- and US-based interdisciplinary teams. In this role, you will build ground-breaking, state-of-the-art causal inference models to guide multi-billion-dollar investment decisions around the global Amazon marketplaces. You will own, execute, and expand a research roadmap that connects science, business, and engineering and contributes to Amazon's long term success. As one of the first economists outside North America/EU, you will make an outsized impact to our international marketplaces and pioneer in expanding Amazon’s economist community in Asia. The ideal candidate will be an experienced economist in empirical industrial organization, labour economics, econometrics, or related structural/reduced-form causal inference fields. You are a self-starter who enjoys ambiguity in a fast-paced and ever-changing environment. You think big on the next game-changing opportunity but also dive deep into every detail that matters. You insist on the highest standards and are consistent in delivering results. Key job responsibilities Work with Product, Finance, Data Science, and Data Engineering teams across the globe to deliver data-driven insights and products for regional and world-wide launches. Innovate on how Amazon can leverage data analytics to better serve our customers through selection and pricing. Contribute to building a strong data science community in Amazon Asia.
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time economics employment at Amazon.
US, CA, Cupertino
We're looking for an Applied Scientist to help us secure Amazon's most critical data. In this role, you'll work closely with internal security teams to design and build AR-powered systems that protect our customers' data. You will build on top of existing formal verification tools developed by AWS and develop new methods to apply those tools at scale. You will need to be innovative, entrepreneurial, and adaptable. We move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality. Inclusive Team Culture Here at AWS, 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. Work/Life Balance 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 flow 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. Mentorship & Career Growth 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. Key job responsibilities Deeply understand AR techniques for analyzing programs and other systems, and keep up with emerging ideas from the research community. Engage with our customers to develop understanding of their needs. Propose and develop solutions that leverage symbolic reasoning services and concepts from programming languages, theorem proving, formal verification and constraint solving. Implement these solutions as services and work with others to deploy them at scale across Payments and Healthcare. Author papers and present your work internally and externally. Train new teammates, mentor others, participate in recruiting and interviewing, and participate in our tactical and strategic planning. About the team Our small team of applied scientists works within a larger security group, supporting thousands of engineers who are developing Amazon's payments and healthcare services. Security is a rich area for automated reasoning. Most other approaches are quite ad-hoc and take a lot of human effort. AR can help us to reason deliberately and systematically, and the dream of provable security is incredibly compelling. We are working to make this happen at scale. We partner closely with our larger security group and with other automated reasoning teams in AWS that develop core reasoning services.
US, NY, New York
Search Thematic Ad Experience (STAX) team within Sponsored Products is looking for a leader to lead a team of talented applied scientists working on cutting-edge science to innovate on ad experiences for Amazon shoppers!. You will manage a team of scientists, engineers, and PMs to innovate new widgets on Amazon Search page to improve shopper experience using state-of-the-art NLP and computer vision models. You will be leading some industry first experiences that has the potential to revolutionize how shopping looks and feels like on Amazon, and e-commerce marketplaces in general. You will have the opportunity to design the vision on how ad experiences look on Amazon search page, and use the combination of advanced techniques and continuous experimentation to realize this vision. Your work will be core to Amazon’s advertising business. You will be a significant contributor in building the future of sponsored advertising, directly impacting the shopper experience for our hundreds of millions of shoppers worldwide, while delivering significant value for hundreds of thousands of advertisers across the purchase journey with ads on Amazon. Key job responsibilities * Be the technical leader in Machine Learning; lead efforts within the team, and collaborate and influence across the organization. * Be a critic, visionary, and execution leader. Invent and test new product ideas that are powered by science that addresses key product gaps or shopper needs. * Set, plan, and execute on a roadmap that strikes the optimal balance between short term delivery and long term exploration. You will influence what we invest in today and tomorrow. * Evangelize the team’s science innovation within the organization, company, and in key conferences (internal and external). * Be ruthless with prioritization. You will be managing a team which is highly sought after. But not all can be done. Have a deep understanding of the tradeoffs involved and be fierce in prioritizing. * Bring clarity, direction, and guidance to help teams navigate through unsolved problems with the goal to elevate the shopper experience. We work on ambiguous problems and the right approach is often unknown. You will bring your rich experience to help guide the team through these ambiguities, while working with product and engineering in crisply defining the science scope and opportunities. * Have strong product and business acumen to drive both shopper improvements and business outcomes. A day in the life * Lead a multidisciplinary team that embodies “customer obsessed science”: inventing brand new approaches to solve Amazon’s unique problems, and using those inventions in software that affects hundreds of millions of customers * Dive deep into our metrics, ongoing experiments to understand how and why they are benefitting our shoppers (or not) * Design, prototype and validate new widgets, techniques, and ideas. Take end-to-end ownership of moving from prototype to final implementation. * Be an advocate and expert for STAX science to leaders and stakeholders inside and outside advertising. About the team We are the Search thematic ads experience team within Sponsored products - a fast growing team of customer-obsessed engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives to drive value for both our customers and advertisers, through continuous innovation. We focus on new ads experiences globally to help shoppers make the most informed purchase decision while helping shortcut the time to discovery that shoppers are highly likely to engage with. We also harvest rich contextual and behavioral signals that are used to optimize our backend models to continually improve the shopper experience. We obsess about our customers and are continuously seeking opportunities to delight them.
US, CA, Palo Alto
Amazon is the 4th most popular site in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. We are working on a new initiative to transform our search engine into a shopping engine that assists customers with their shopping missions. We look at all aspects of search CX, query understanding, Ranking, Indexing and ask how we can make big step improvements by applying advanced Machine Learning (ML) and Deep Learning (DL) techniques. We’re seeking a thought leader to direct science initiatives for the Search Relevance and Ranking at Amazon. This person will also be a deep learning practitioner/thinker and guide the research in these three areas. They’ll also have the ability to drive cutting edge, product oriented research and should have a notable publication record. This intellectual thought leader will help enhance the science in addition to developing the thinking of our team. This leader will direct and shape the science philosophy, planning and strategy for the team, as we explore multi-modal, multi lingual search through the use of deep learning . We’re seeking an individual that can enhance the science thinking of our team: The org is made of 60+ applied scientists, (2 Principal scientists and 5 Senior ASMs). This person will lead and shape the science philosophy, planning and strategy for the team, as we push into Deep Learning to solve problems like cold start, discovery and personalization in the Search domain. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon [Earth's most customer-centric internet company]. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California.