ICASSP: What “signal processing” has come to mean

Alexa scientist Ariya Rastrow on the blurring boundaries between acoustic processing and language understanding.

The International Conference on Acoustics, Speech, and Signal Processing (ICASSP), which starts today, is now in its 45th year, and according to Google Scholar’s rankings, it’s the highest-impact conference in the field of signal processing.

But as speech-related technologies have matured, the definition of signal processing has expanded. “ICASSP is a mix of a lot of different tracks,” says Ariya Rastrow, an Alexa principal research scientist who attended his first ICASSP in 2006. “It has the whole spectrum, from very low-level signal processing all the way to interpretation and natural-language understanding.”

Ariya Rastrow.png
Alexa senior principal scientist Ariya Rastrow
Credit: Jordan Stead

This diversity, Rastrow explains, simply reflects that of the human audio-processing system. The brain doesn’t rely exclusively on acoustic signals to recognize words, and neither should computer systems.

“The interaction between language and acoustics is very dynamic from the human perspective,” Rastrow says. “If I’m talking to you in a very clean environment, we are capable of following on the acoustic level at very high resolution. But if we’re sitting in a noisy bar, you as a human are going to rely more on your prior — on a semantic level, what are the things that the other person might say? what are the topics that they might talk about? —and use that to enhance your recognition.”

Traditionally, the task of spoken-language understanding has been broken into two components: automatic speech recognition (ASR), which converts an acoustic speech signal into text, and natural-language understanding (NLU), which makes sense of the text.

But in fact, speech recognition usually relies on higher-level linguistic features to identify words. The traditional ASR system consists of an acoustic model, which translates acoustic signals into low-level phonetic representations; a lexicon, which maps sequences of low-level phonetic representations to words; and a language model, which uses high-level statistics about words’ co-occurrence to adjudicate between competing interpretations of the acoustic signal.

“Twenty, twenty-five years ago, there was this pragmatic idea to build factored systems,” Rastrow explains. “You have clear-cut boundaries between components of the system. Traditional speech recognition systems are built over an architecture that we call a hidden Markov model (HMM) architecture. The HMM architecture will put these multiple knowledge sources together at inference time. But the acoustic model and the language model are trained separately.”

Shared representations

Recently, however, this approach has begun to give way to end-to-end training of large, neural-network-based architectures. That is, a single neural network is trained on examples that consist of acoustic inputs and fully transcribed outputs, and it directly learns the relationships previously encoded in the ASR system’s separate components.

“This has many benefits” Rastrow says, “one being that by doing joint training you build systems that are more optimized in terms of accuracy. If you build factored systems, often you train each component for a specific objective function, and at inference time, they don’t know how to handle disfluencies and errors. By virtue of advances in architectures and doing joint training and multitask training, the systems are becoming more robust to those types of confusions.”

“That’s one benefit,” Rastrow continues. “Another is that the system gains in efficiency. By having a mechanism to do knowledge transfer, joint training, or shared representation, you get to the point where different parts of the systems can rely on the same types of representations or shared layers [of the network]. This can result in compression of the overall size of the system, execution speedups, and opportunities to deploy such systems on low-resource devices and hardware.

“For example, if you’re doing acoustic-event detection, and you’re also doing wake word detection and whisper detection, which are different types of audio-based classification tasks, one way is to build all the systems separately. The other way is that you can do knowledge transfer and shared representation learning, and by virtue of those shared network components and layers, you can gain efficiency beyond the obvious accuracy improvements.

“Also, the whole system is done in neural-network execution that we know how to accelerate both on the software and the hardware side, versus this explicit knowledge representation — lexicon versus language model. Traditionally, these are not deep-learning based, so we could not leverage these efficiency mechanisms. For the last two to three years, we have been pursuing this direction.”

Total integration

Allowing a single large model to integrate the ASR system’s low-level acoustic-signal processing and high-level language modeling raises the prospect of taking advantage of still higher-level linguistic features. In one of the 19 Amazon papers at this year’s ICASSP, for instance, Alexa researchers report using semantic features to help distinguish between utterances intended for Alexa and those that are not, where in the past, Alexa’s “device directedness” detector relied solely on acoustic features.

The end point of all this integration, of course, would be a single neural network that executed the entire task of spoken-language understanding — both ASR and NLU.

“There is emerging research that shows that at least for a subset of interactions, you can build a single, small-footprint network that can directly translate audio to the semantic level,” Rastrow says. “You get even better latency. You don’t have to do stage-wise execution. Also, there are studies showing that humans don’t do recognition word by word. We carry information on the parts of the speech that are semantically important for the topic, for the conversation.”

“But challenges remain,” Rastrow says. “These all-neural systems thrive on data. And once you move closer to the understanding layer, you have to cope more and more with data sparsity and the nuances of unique interactions. On the acoustic level, for the sound <p>, even across languages, you can get a lot of examples. But as you go closer to the semantic and sentence-level understanding, the patterns become more unique.

“One challenge is how we combine these new architectures for doing direct audio to NLU with our advances in semi-supervised learning and unsupervised learning. Another challenge is how to combine very data-oriented learning systems with some kind of reasoning or logic.

“I’ll give you an example. If you say, ‘Alexa turn on the bedroom light’, and Alexa misinterprets and turns on the kitchen light, and you follow that by saying, ‘No, Alexa, don’t turn on the kitchen light,’ now you have the negation problem. When you say ‘Don’t turn it on’, you really mean ‘Turn it off’. It is very hard to find those examples in data. Traditionally, we know how to address that problem with rules and logic and reasoning, but relying merely on data might not give us a good representation of those unique patterns. So the questions in the next two, three years of research will be how to combine those systems with either semi-supervised or unsupervised learning and how to combine them with knowledge and logic.”

Amazon publications at ICASSP

View All View All
Raw Waveform-Based End-to-End Deep Convolutional Network for Spatial Localization of Multiple Acoustic Sources
In this paper, we present an end-to-end deep convolutional neural network operating on multi-channel raw audio data to localize multiple simultaneously active acoustic sources in space. Previously reported deep-learning-based approaches work well in localizing a single source directly from multi-channel raw audio but are not easily extendable to localize multiple sources due to the well-known permutation
Using VAEs and Normalizing Flows for One-Shot Text-to-Speech Synthesis of Expressive Speech
We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a Variational Auto Encoder (VAE) and a Householder Flow. The proposed system provides a 22% KL-divergence reduction while jointly improving perceptual metrics
Pseudo Labeling and Negative Feedback Learning for Large-scale Multi-label Domain Classification
In large-scale domain classification, an utterance can be handled by multiple domains with overlapped capabilities. However, only a limited number of ground-truth domains are provided for each training utterance in practice, while knowing as many correct target labels as possible is helpful for improving the model performance. In this paper, given one ground-truth domain for each training utterance, we
Improving Device Directedness Classification of Utterances with Semantic Lexical Features
User interactions with personal assistants like Alexa, Google Home and Siri are typically initiated by a wake term or wakeword. Several personal assistants feature “follow-up” modes that allow users to make additional interactions without the need of a wakeword. For the system to only respond when appropriate, and to ignore speech not intended for it, utterances must be classified as device-directed or

Related content

US, CA, Santa Clara
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere 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 BalanceOur 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 GrowthOur 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.
IN, TS, Hyderabad
Job summaryAre you excited about driving business growth for millions of sellers by applying Machine Learning? Do you thrive in a fast-moving, large-scale environment that values data-driven decision making and sound scientific practices? We are looking for experienced data scientists to build sophisticated decision making systems that help Amazon Marketplace Sellers to grow their businesses.Amazon Marketplace enables sellers to reach hundreds of millions of customers and provides sellers the tools and services needed to make e-commerce simple, efficient and successful. Our team builds the core intelligence, insights, and algorithms that power a range of products used by millions of sellers. We are tackling large-scale, challenging problems such as helping sellers to prioritise business tasks by bringing together petabytes of data from sources across Amazon.You will be proficient with creating value out of data by formulating questions, analysing vast amounts of data, and communicating insights effectively to audience of varied backgrounds. In addition, you'll contribute to online experiments, build machine learning pipelines and personalised data products.To know more about Amazon science, Please visit https://www.amazon.scienceKey job responsibilities· Collaborate with domain experts, formulate questions, gather, process and analyse petabytes of data to unearth reliable insights· Design & execute experiments and analyze experimental results· Communicate insights effectively to audience of a wide range of backgrounds· Formulate relevant prediction problems and solve them by developing machine learning models· Partner with data engineering teams to improve quality of data assets, metrics and insights· Leverage industry best practices to establish repeatable science practices, principles & processes
US, NY, New York
Job summaryJob summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere 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 BalanceOur 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 GrowthOur 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.
US, WA, Seattle
Job summaryAmazon Sub-Same-Day Supply Chain team is looking for an experienced and motivated Senior Data Scientist to generate data-driven insights influencing the long term SSD supply chain strategy, build the necessary predictive models, optimization algorithms and customer behavioral segments allowing us to discover and build the roadmap for SSD to enable operational efficiency and scale.Key job responsibilitiesWork with product managers, engineers, other scientists, and leadership to identify and prioritize complex problems.Translate business problems into specific analytical questions and form hypotheses that can be answered with available data using scientific methods or identify additional data needed in the master datasets to fill any gapsDesign, develop, and evaluate highly innovative statistics and ML modelsGuide and establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementationProactively seek to identify business opportunities and insights and provide solutions to shape key business processes and policies based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams.A day in the lifeIn this role, you will be a technical expert with significant scope and impact. You will work with Product Managers, Business Engineers, and other Scientists, to deeply understand SSDs current optimization strategy while benchmarking against industry best practices and standards to gain insights that will drive our roadmap. A successful Data Scientist will have extreme bias for action needed in a startup environment, with outstanding leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. It will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term scientific solutions. We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career.About the teamAmazon's Sub-Same Day (SSD) delivery program is designed to get customers their items as fast as possible – currently in as quickly as five hours. With ultra-fast delivery becoming increasingly important, we are looking for an experienced Senior Data Scientist to help us benchmark against industry standards to uncover insights to improve and optimize the long term supply chain strategy for Amazons Sub-Same-Day business.
US, WA, Seattle
Job summaryAmazon's Weblab team enables experimentation at massive scale to help Amazon build better products for customers. A/B testing is in Amazon's DNA and we're at the core of how Amazon innovates on behalf of customers. We are seeking a skilled Applied Scientist to help us build the future of experimentation systems at Amazon.About you:You have an entrepreneurial spirit and want to make a big impact on Amazon and its customers. You are excited about cutting-edge research on unsupervised learning, graph algorithms, and causal inference in the intersection between Machine Learning, Statistics, and Econometrics. You enjoy building massive scale and high performance systems but also have a bias for delivering simple solutions to complex problems. You're looking for a career where you'll be able to build, to deliver, and to impress. You challenge yourself and others to come up with better solutions. You develop strong working relationships and thrive in a collaborative team environment.About us together:We're going to help Amazon make better long term decisions by designing and delivering A/B-testing systems for long-term experiments, and by using these systems to figure out how near term behavior impacts long term growth and profitability. Our work will inform some of the biggest decisions at Amazon. Along the way, we're going to face seemingly insurmountable challenges. We're going to argue about how to solve them, and we'll work together to find a solution that is better than each of the proposals we came in with. We'll make tough decisions, but we'll all understand why. We'll be the dream team.We have decades of combined experience on the team in many areas science and engineering so it's a great environment in which to learn and grow. A/B testing is one of the hottest areas of research and development in the world today and this is a chance to learn how it works in the company known for pioneering its use.
US, VA, Arlington
Job summaryAmazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.Sponsored Products helps merchants, retail vendors, and brand owners succeed via native advertising that grows incremental sales of their products sold through Amazon. The Sponsored Products Ad Marketplace organization optimizes the systems and ad placements to match advertiser demand with publisher supply using a combination of machine learning, big data analytics, ultra-low latency high-volume engineering systems, and quantitative product focus. Our goals are to help buyers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and to build a major, sustainable business that helps Amazon continuously innovate on behalf of all customers.We are seeking a Sr. Applied Science Manager who has a solid background in applied Machine Learning and AI, deep passion for building data-driven products, ability to communicate data insights and scientific vision, and has a proven track record of leading both applied scientists and software engineers to execute complex projects and deliver business impacts.In this team, Machine Learning and Deep Learning technologies including Semantic Retrieval, Natural Language Processing (NLP), Information Extraction, Image Understanding, Learning to Rank are used to match shoppers' search queries to ads with per impression prediction models that run in real-time with tight latency budgets. Models are trained using self-supervised techniques, transfer learning, and supervised training using labeled datasets. Knowledge distillation and model compression techniques are used to optimize model performance for production serving.The Senior Manager role will lead science and engineering efforts in these areas for Amazon Search pages WW. The person in this role is responsible for: maintaining the consistent and long term reliability for the models and the delivery services that power them, managing diverse teams across multiple domains, and collaborating cross-functional with other senior decision makers. Our critical LPs for this role are Think Big, Are Right A lot, and Earns Trust. What is key is that the leader will need a dynamic mindset to build systems that are flexible and will scale.In this role, you will:· Lead a group of both applied scientists and software engineers to deliver machine-learning and AI solutions to production.· Advance team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner.· Develop science and engineering roadmap, run Sprint/quarter and annual planning, and foster cross-team collaboration to execute complex projects.· Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.· Hire and develop top talents, provide technical and career development guidance to both scientists and engineers in the organization.Locations: Seattle, WA; New York, NY; Arlington, VA
US, NY, New York
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere 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 BalanceOur 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 GrowthOur 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.
US, WA, Seattle
Job summaryWorkforce Staffing (WFS) brings together the workforce powering Amazon’s ability to delight customers: the Amazon Associate. With over 1M hires, WFS supports sourcing, hiring, and developing the best talent to work in our fulfillment centers, sortation centers, delivery stations, shopping sites, Prime Air locations, and more.WFS' Funnel Science and Analytics team is looking for a Research Scientist. This individual will be responsible for conducting experiments and evaluating the impact of interventions when conducting experiments is not feasible. The perfect candidate will have the applied experience and the theoretical knowledge of policy evaluation and conducting field studies.Key job responsibilitiesAs a Research Scientist (RS), you will do causal inference, design studies and experiments, leverage data science workflows, build predictive models, conduct simulations, create visualizations, and influence science and analytics practice across the organization.Provide insights by analyzing historical data from databases (Redshift, SQL Server, Oracle DW, and Salesforce).Identify useful research avenues for increasing candidate conversion, test, and create well written documents to communicate to technical and non-technical audiences.About the teamFunnel Science and Analytics team finds ways to maximize the conversion and early retention of every candidate who wants to be an Amazon Associate. By focusing on our candidates, we improve candidate and business outcomes, and Amazon takes a step closer to being Earth’s Best Employer.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles).Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt 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. 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.Work/Life BalanceOur 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 GrowthOur 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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles).Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt 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. 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.Work/Life BalanceOur 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 GrowthOur 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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.