Alexa enters the “age of self”

More-autonomous machine learning systems will make Alexa more self-aware, self-learning, and self-service.

Alexa launched in 2014, and in the more than six years since, we’ve been making good on our promise to make Alexa smarter every day. In addition to foundational improvements in Alexa’s core AI technologies, such as speech recognition and natural-language-understanding systems, Alexa scientists have developed technologies that continue to delight our customers, such as whispered speech and Alexa’s new live translation service.

Prem Natarajan, Alexa AI vice president of natural understanding, giving a presentation
Prem Natarajan, Alexa AI vice president of natural understanding, at a conference in 2018.

But some of the technologies we’ve begun to introduce, together with others we’re now investigating, are harbingers of a step change in Alexa’s development — and in the field of AI itself. Collectively, these technologies will bring a new level of generalizability and autonomy to both the Alexa voice service and the tools available to Alexa developers, ushering in what I like to think of as a new “age of self” in artificial intelligence, an age in which AI systems such as Alexa become more self-aware and more self-learning, and in which they lend themselves to self-service by experienced developers and even end users.

By self-awareness, I mean the ability to maintain an awareness of ambient state (e.g., time of day, thermostat readings, and recent actions) and to employ commonsense reasoning to make inferences that reflect that awareness and prior/world knowledge. Alexa hunches can already recognize anomalies in customers’ daily routines and suggest corrections — noticing that a light was left on at night and offering to turn it off, for instance. Powered by commonsense reasoning, self-awareness goes further: for instance, if a customer turns on the television five minutes before the kids’ soccer practice is scheduled to end, an AI of the future might infer that the customer needs a reminder about pickup.

Smart home.png
In the "age of self", AIs will be able to infer customers’ implicit intentions from observable temporal patterns, such as interactions with smart-home devices like thermostats, door locks, and lights.

Self-learning is Alexa’s ability to improve and expand its capabilities without human intervention. And like self-awareness, self-learning employs reasoning: for example, does the customer’s response to an action indicate dissatisfaction with that action? Similarly, when a customer issues an unfamiliar command, a truly self-learning Alexa would be able to infer what it might mean — perhaps by searching the web or exploring a knowledge base — and suggest possibilities.

Self-service means, essentially, the democratization of AI. Alexa customers with no programming experience should be able to customize Alexa’s services and even create new Alexa capabilities, and skill developers without machine learning experience should be able to build complex yet robust conversational skills. Colloquially, these are the conversational-AI equivalents of no-code and low-code development environments.

To be clear, the age of self is not yet upon us, and its dawning will require the maturation of technologies still under development, at Amazon and elsewhere. But some of Alexa’s recently launched capabilities herald a lightening in the Eastern sky.

Self-awareness

In 2018, we launched Alexa hunches for the smart home, with Alexa suggesting actions to take in response to anomalous sensor data. By early 2021, the science has advanced adequately for us to launch an opt-in service in which Alexa can take action immediately and automatically. In the meantime, we’ve also been working to expand hunches to Alexa services other than the smart home.

Technologies will bring a new level of generalizability and autonomy to both the Alexa voice service and the tools available to Alexa developers, ushering in what I like to think of as a new 'age of self' in artificial intelligence.
Prem Natarajan

But commonsense reasoning requires something more — the ability to infer customers’ implicit intentions from observable temporal patterns. For instance, what does it mean if the customer turns down the thermostat, turns out the lights, locks the front door, and opens the garage? What if the customer initiates an interaction with a query like “Alexa, what’s playing at Rolling Hills Cine Plaza?”

In 2020, we took steps toward commonsense reasoning with a new Alexa function that can infer a customer’s latent goal— the ultimate aim that lies behind a sequence of requests. When a customer asks for the weather at the beach, for instance, Alexa might use that query, in combination with other contextual information, to infer that the customer may be interested in a trip to the beach. Alexa could then offer the current driving time to the beach.

To retrieve that information, Alexa has to know to map the location of the weather request to the destination variable in the route-planning function. This illustrates another aspect of self-awareness: the ability to track information across contexts.

That ability is at the core of the night-out experience we’ve developed, which engages the customer in a multiturn conversation to plan a complete night out, from buying movie tickets to making restaurant and ride-share reservations. The night-out experience tracks times and locations across skills, revising them on the fly as customers evaluate different options. To build the experience, we leveraged the machinery of Alexa Conversations, a service that enables developers to quickly and easily create dialogue-driven skills, and we drew on our growing body of research on dialogue state tracking.

Slot_tracking.png._CB436837753_.png
Dialogue states at several successive dialogue turns

Self-awareness, however, includes an understanding not only of the conversational context but also of the customer’s physical context. In 2020, we demonstrated natural turn-taking on Alexa-enabled devices with cameras. When multiple speakers are engaging with Alexa, Alexa can use visual cues to distinguish between speech the customers are directing at each other and speech they’re directing at Alexa. In ongoing work, we’re working to expand this functionality to devices without cameras, by relying solely on acoustic and linguistic signals.

Finally, self-awareness also entails the capacity for self-explanation. Today, most machine learning models are black boxes; even their creators have no idea how they’re doing what they do. That uncertainty has turned explainable or interpretable AI into a popular research topic.

Amazon actively publishes on explainable-AI topics. In addition, the Alexa Fund, an Amazon venture capital investment program, invested in fiddler.ai, a startup that uses techniques based on the game-theoretical concept of Shapley values to do explainable AI.

Self-learning

Historically, the AI development cycle has involved collection of data, annotation of that data, and retraining of models on the newly annotated data — all of which add up to a laborious process.

In 2019, we launched Alexa’s self-learning system, which automatically learns to correct errors — both customer errors and errors in Alexa’s language-understanding models — without human involvement. The system relies on implicit signals that a request was improperly handled, as when a customer interrupts a response and rephrases the same request.

Absorbing-Markov-chain models for three different sequences of utterances
Alexa's self-learning system models customer interactions with Alexa as sequences of states; different customer utterances (u0, u1, u2) can correspond to the same state (h0). The final state of a sequence, known as the "absorbing state", indicates the success (checkmark) or failure (X) of a transaction.
Stacy Reilly

Currently, that fully automatic system is correcting 15% of defects. But those are defects that occur across a spectrum of users; only when enough people implicitly identify the same flaw does the system address it. We are working to adapt the same machinery to individual customers’ preferences — so that, for instance, Alexa can learn that when a particular customer asks for the song “Wow”, she means not the Post Malone hit from 2019 but the 1978 Kate Bush song.

Customers today also have the option of explicitly teaching Alexa their preferences. In the fall of 2020, we launched interactive teaching by customers, a capability that enables customers to instruct Alexa how they want certain requests to be handled. For instance, the customer can teach Alexa that the command “reading mode” means lights turned all the way up, while “movie mode” means only twenty percent up.

Self-service

Interactive teaching is also an early example of how Alexa is enabling more self-service. It extends prior Alexa features, like blueprints, which let customers build their own simple skills from preexisting templates, and routines, which let customers chain together sequences of actions under individual commands.

In March 2021, we announced the public release of Alexa Conversations, which allows developers to create dialogue-driven skills by uploading sample dialogues. Alexa Conversations’ sophisticated machine learning models use those dialogues as templates for generating larger corpora of synthetic training data. From that data, Alexa Conversations automatically trains a machine learning model.

Alexa Conversations does, however, require the developer to specify the set of entities that the new model should act upon and an application programming interface for the skill. So while it requires little familiarity with machine learning, it assumes some programming experience. 

ambiguous_slots.gif._CB438712971_.gif
An Alexa feature known as catalogue value suggestions suggests entity names to skill developers on the basis of their "embeddings", or locations in a representational space. If the embeddings of values (such as bird, dog, or cat) for a particular entity type are close enough (dotted circles) to their averages (solid circle and square), the system suggests new entity names; otherwise, it concludes that suggestions would be unproductive.
Animation by Nick Little

We are steadily chipping away at even that requirement, by making development for Alexa easier and more intuitive. As Alexa’s repertory of skills grows, for instance, entities are frequently reused, and we already have systems that can inform developers about entity types that they might not have thought to add to their skills. This is a step toward a self-service model in which developers no longer have to provide exhaustive lists of entities — or, in some cases, any entities at all.

Another technique that makes it easier to build machine learning models is few-shot learning, in which an existing model is generalized to a related task using only a handful of new training examples. This is an active area of research at Alexa: earlier this year, for example, we presented a paper at the Spoken Language Technologies conference that described a new approach to few-shot learning for natural-language-understanding tasks. Compared to its predecessors, our approach reduced the error rate on certain natural-language-understanding tasks by up to 12.4%, when each model was trained on only 10 examples.

These advances, along with the others reported on Amazon Science, demonstrate that the Alexa AI team continues to accelerate its pace of invention. More exciting announcements lie just over the horizon. I’ll be stopping back here every once in a while to update you on Alexa’s journey into the age of self.

About the Author
Prem Natarjan is the Alexa AI vice president of natural understanding

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Alexa is the groundbreaking voice service that powers Echo and other devices designed around your voice. Our team is creating the science and technology behind Alexa. We’re working hard, having fun, and making history. Come join our team! You will have an enormous opportunity to impact the customer experience, design, architecture, and implementation of a cutting edge product used every day by people you know.We’re looking for a passionate, talented, and inventive scientist to help build industry-leading conversational technologies that customers love. Our team's mission is the enable Alexa to understand sounds and vocalization beyond speech. As a Senior Applied Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in speech and audio processing. Your work will directly impact our customers in the form of novel products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. You will mentor junior scientists, create and drive new initiatives.
US, CO, Boulder
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of 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 and break fresh ground in product and technical innovations every day!We are looking for an Applied Scientist to join the Audience Metrics and Telemetry team in Boulder, CO.What we do: Advertisers need to create targeted, personalized experiences for people if they want to grab their attention. To help match ads to the right users, our engineering teams curate so-called "audiences", which group people with similar interests, lifestyle, demographics, or purchase intentions. Our services ingest billions of behavioral signals every day, apply machine learning and statistical models to package these signals into audiences, and decorate them for every ad request received within a fraction of a second. This allows ad campaigns to run more effectively and to adjust the messaging for different customer groups. Audience Metrics and Telemetry owns lifecycle and performance management for millions of audiences, running A/B experiments to quantify incremental lift in performance and ensuring that our audience portfolio is first rate.What to expect: As a scientist on the team, you can be involved in every aspect of the process - from idea generation, business analysis and scientific research, through to development and deployment of advanced models. We expect you to deliver end-to-end solutions rather than algorithm prototypes and you will work closely with the other scientists and engineers in the organization to productionize, scale, and deploy your models world-wide. A successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and with great leadership and communication skills. In turn, we offer an exceptional opportunity to grow your technical and non-technical skills and make a real difference to the Amazon Advertising business.As on Applied Scientist on the Audience Metrics and Telemetry team, you will:· Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgment.· Research and implement novel machine learning and statistical approaches.· Report results in a manner which is both statistically rigorous and compelling to read for non-scientists· Collaborate with software engineering teams to integrate successful experimental results into large-scale, highly complex Amazon production systems.· Contribute to Amazon's Intellectual Property through patents and/or external publications.· Research optimal ML models, build ML models, perform proof-of-concept and deploy ML models into production.Work environment: The position is based in sunny Boulder, CO with easy access to the mountains. We recognize the benefits of a more flexible schedule and welcome work-from-home a couple of days per week.Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team is responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists and Engineers developing models to predict customer behavior and optimize the customer experience with Amazon Prime. This includes identifying who our customers are and providing them with personalized relevant content. As an ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions.There are numerous scientific and technical challenges you will get to tackle in this role, such as global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, cohort identification in a semi-supervised setting, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from supervised learning, bandits, optimization, and RL.As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Redshift, Sagemaker, DynamoDB, S3, ...), various ML algorithms and techniques (XGBoost, Random Forests, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques.Major responsibilities· · Build and develop machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams.· · Leverage Bandits and Reinforcement Learning for Recommendation Systems.· · Develop offline policy estimation tools and integrate with reporting systems.· · Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.· · Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.· · Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.· · Use machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.· · Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
US, WA, Seattle
In the Amazon Product Knowledge Team, we are building comprehensive schematic and semantic constructs to understand customer intent, in order to provide a delightful experience that feels targeted to their shopping mission. It expands beyond factual product characteristics (e.g. resolution of a TV) to additional dimensions used in customer shopping missions: what the product is used for (e.g. baby-proofing), where the product is used (e.g. kitchen), who uses the product (e.g. teenager), when the product is used (e.g. thanksgiving), and opinions about the product (e.g. cute t-shirt). We build scalable solutions that are partially or entirely powered by AI and ML to discover Product Knowledge by mining customer engagements (e.g. search queries, customer reviews, web pages … etc.).We have multiple positions for applied scientists who are excited to work on big data challenges including; web scale data integration, natural language processing, discovery of new relationships along with their semantics, knowledge inferencing and enhancement, knowledge embedding, entity recognition, and improving data quality to support strategic and tactical decision-making in building Product Knowledge.We are looking for applied scientists with experience in building practical solutions who can work closely with software engineers to ship and automate solutions in production. Our applied scientists also collaborate and partner with teams across Amazon to understand and reflect on how to create benefit for every customer.
US, WA, Seattle
In the Amazon Product Knowledge Team, we are building comprehensive schematic and semantic constructs to understand customer intent, in order to provide a delightful experience that feels targeted to their shopping mission. It expands beyond factual product characteristics (e.g. resolution of a TV) to additional dimensions used in customer shopping missions: what the product is used for (e.g. baby-proofing), where the product is used (e.g. kitchen), who uses the product (e.g. teenager), when the product is used (e.g. thanksgiving), and opinions about the product (e.g. cute t-shirt). We build scalable solutions that are partially or entirely powered by AI and ML to discover Product Knowledge by mining customer engagements (e.g. search queries, customer reviews, web pages … etc.).We have multiple positions for applied scientists who are excited to work on big data challenges including; web scale data integration, natural language processing, discovery of new relationships along with their semantics, knowledge inferencing and enhancement, knowledge embedding, entity recognition, and improving data quality to support strategic and tactical decision-making in building Product Knowledge.We are looking for applied scientists with experience in building practical solutions who can work closely with software engineers to ship and automate solutions in production. Our applied scientists also collaborate and partner with teams across Amazon to understand and reflect on how to create benefit for every customer.
US, WA, Seattle
Amazon Web Services (AWS) Renewable Energy Team is looking for Data Scientist to develop new solutions and analytics towards delivering 100% renewable energy to our global infrastructure. You will utilize innovative data science, machine-learning, and distributed software on the Cloud to build systems that automate and optimize delivery of our renewable energy to our load under the uncertainty of demand, pricing and supply.The ideal candidates will have a deep knowledge of quantitative modelling in at least one area (ML, optimization, multivariate statistics), the ability to build and refine prototype models that can be implemented in production, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the excitement to take iterative approaches to tackle big, long-term problems.This is an exciting role part of a program focused on optimizing our renewable assets across the globe. You will work closely with the energy team on large-scale renewable projects and help AWS achieve its 100% renewable energy goals.
CA, BC, Vancouver
Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.Please visit https://www.amazon.science for more information.Amazon has built a reputation for excellence with recent examples of being named the #1 most trusted company for customers. To deliver on this reputation for trust, the Seller Partner Abuse team is tasked with identifying and preventing abuse for our customers and brand owners worldwide.Seller Partner Abuse is seeking an innovative, results-oriented, customer-centric data scientist to drive expansion of innovative ML products globally in the Risk space. As a Data Scientist, you will be responsible for modeling complex problems, discovering insights and identifying opportunities through the use of statistical, machine learning, algorithmic, data mining and visualization techniques. You will need to collaborate effectively with internal stakeholders and cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards. The candidate should be able to apply a breadth of tools, data sources and analytical techniques to answer a wide range of high-impact business questions and present the insights in concise and effective manner. Additionally, the candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high impact role with goals that directly impacts the bottom line of the business.Responsibilities:· Use predictive analytics and machine learning techniques to solve complex problems and drive business decisions.· Employ the appropriate algorithms to discover patterns of risks, and prevent abuse· Design experiments, test hypotheses, and build actionable models to optimize SPA policies and operations· Solve analytical problems, and effectively communicate methodologies and results both in writing and verbal· Build predict models to forecast risks for product launches and help predict workflow and capacity requirements for SPA· Draw inferences and conclusions, and create dashboards and visualizations of processed data, identify trends, anomalies· Work closely with internal stakeholders such as business teams, engineering teams, and partner teams and align them with respect to your focus areaAmazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.
US, CA, Sunnyvale
The principal scientist will initiate and work on key initiatives for developing and advancing world-leading automatic speech recognition (ASR) technology for any voice-driven Alexa end-point. The goal is to achieve unmatched speech recognition accuracy for any device, in any acoustic environment, for any speaker, and for any domain and application running on Alexa. You will analyze system short-comings, for leading the development of data-driven and algorithmic improvements, for defining the path to production, and for influencing design and architecture of goal-relevant software. You will work in a hybrid, fast-paced organization where scientists and engineers work jointly together and drive improvements directly to production.The principal scientist will either go deep on a specific area like single ASR model recognizing multiple languages supporting in-utterance code switching, or models learning without human transcription and act as a technical lead, or will work across teams and areas influencing data, algorithm, and design decisions. Areas of interest cover the whole ASR spectrum, including general purpose ASR, multi-channel raw audio input acoustic modeling, noise robust acoustic modeling, device and speaker independent acoustic modeling, acoustic model adaptation, advanced deep learning for acoustic and language modeling, active learning and semi-/unsupervised learning techniques for acoustic and language modeling, learning from heterogeneous and mismatched audio and text data including data selection and data simulation, large-scale open-domain language modeling, language model adaptation, contextual and personalized language modeling, multi-lingual automatic pronunciation generation, text verbalization and (inverse) text normalization, etc.The principal scientist will help drive scalable, robust, and automated solutions, making new algorithms and processes scalable to work on production-scale data sizes and achieving automated adaptation of processes and algorithms to new environments and to other locales. You will also help integrate new algorithms and processes into existing modeling stacks, simplify and streamline the existing modeling stacks, and develop testing and evaluation strategies. You will influence design and architecture of software stacks used offline and at runtime for building and deploying ASR model artifacts, achieving flexible yet efficient solutions suitable for R&D work and for running in production.
IN, KA, Bangalore
Job Description:We are seeking a Sr. Data Scientist to be part of the Search Science and Artificial Intelligence team world wide. This is a strategic role to shape and deliver our technical strategy in developing and deploying Machine Learning solutions to our hardest customer facing problems. Our goal is to delight customers by providing a a measure of customer experience. These initiatives are at the heart of the organization and recognized as the innovations that will allow us to build a differentiated product that exceeds customer expectations. We're a high energy, fast growth business excited to have the opportunity to shape Search is defined for years to come. If this role seems like a good fit, please reach out, we'd love to talk to you.This role requires working closely with business, engineering and other scientists within Search, Catalogue and across Amazon to deliver ground breaking product. You will lead high visibility and high impact programs collaborating with various teams across Amazon. You will work with a team of Engineers and Scientists to launch new customer facing features and improve the current features.
IE, CO, Cork
At Amazon we strive to be earth’s most customer centric company and data is a foundational component to making this happen. Are you curious about new platforms and technologies and have a desire to deliver world-class customer service? Are you excited by the idea of owning a problem and innovating on behalf of customers? Can you deal with ambiguity and keep up with the pace of a company whose cycles are measured in weeks, not years?The Device, Digital & Alexa Support (D2AS) team works with some of the fastest growth areas in the company. Amazon introduced the first Kindle in 2007 - since then, we have expanded to become the best-selling e-reader family in the world. We have gone beyond Kindle with our powerhouse Fire tablets, built for work and play with our Fire operating system. For streaming media lovers, we have created Amazon Fire TV, Fire TV Stick, and Fire TV Edition with voice search. Fire TV devices come with access to 500,000 movies, TV shows, and tens of thousands of channels, and apps. In 2014, we introduced Amazon Echo and Alexa, the voice service that powers Echo and other devices so customers can play music, control their smart homes, and get information, news, weather, and more using just their voice. Alexa is now integrated with over 20,000 third party devices from 3,500 brands. Alexa has 50,000 skills and developers in 180 countries.The Device, Digital & Alexa Support (D2AS) – Tech team is looking for a Manager, Data Science. This position will lead innovative science solutions and products to increase Amazon’s ability to personalize Customer Service: from models to understand customer needs, optimization models to identify the best solution, and personalized recommender systems. You will identify specific and actionable opportunities to solve existing Customer problems and develop science based products.The ideal candidate will have outstanding communication skills, strong technical knowledge, proven ability to lead a team scientists, with an innate drive to deliver results. She/he will be comfortable with ambiguity and will enjoy working in a fast-paced environment.Responsibilities:· Lead a team across technical job families - scientists and engineers· Manage a science agenda that balances short term deliverables with measurable business impact with long term projects.· Develop a roadmap/products for ML model deployment· Provide technical and scientific guidance to team members· Hire and develop top talent· Collaborate with business and software teams both within and outside of D2AS
US, CA, San Diego
Do you want to join Alexa AI -- the science team behind Amazon’s intelligence voice assistance system? Do you want to utilize cutting-edge deep-learning and machine learning algorithms to delight millions of Alexa users around the world? If your answers to these questions are “yes”, then come join the Alexa Artificial Intelligence team. We are responsible for the deep learning technology that is central to the automated ranking and arbitration to optimize for end-to-end customer satisfaction.As an Applied Scientist you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.