Alexa at five: looking back, looking forward

Today is the fifth anniversary of the launch of the Amazon Echo, so in a talk I gave yesterday at the Web Summit in Lisbon, I looked at how far Alexa has come and where we’re heading next.

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This poster of the original Echo device, signed by the scientists and engineers who helped make it possible, hangs in Rohit's office.

Amazon’s mission is to be the earth’s most customer-centric company. With that mission in mind and the Star Trek computer as an inspiration, on November 6, 2014, a small multidisciplinary team launched Amazon Echo, with the aspiration of revolutionizing daily convenience for our customers using artificial intelligence (AI).

Before Echo ushered in the convenience of voice-enabled ambient computing, customers were used to searches on desktops and mobile phones, where the onus was entirely on them to sift through blue links to find answers to their questions or connect to services. While app stores on phones offered “there’s an app for that” convenience, the cognitive load on customers continued to increase.

Alexa-powered Echo broke these human-machine interaction paradigms, shifting the cognitive load from customers to AI and causing a tectonic shift in how customers interact with a myriad of services, find information on the Web, control smart appliances, and connect with other people.

Enhancements in foundational components of Alexa

In order to be magical at the launch of Echo, Alexa needed to be great at four fundamental AI tasks:

  1. Wake word detection: On the device, detect the keyword “Alexa” to get the AI’s attention;
  2. Automatic speech recognition (ASR): Upon detecting the wake word, convert audio streamed to the Amazon Web Services (AWS) cloud into words;
  3. Natural-language understanding (NLU): Extract the meaning of the recognized words so that Alexa can take the appropriate action in response to the customer’s request; and
  4. Text-to-speech synthesis (TTS): Convert Alexa’s textual response to the customer’s request into spoken audio.

Over the past five years, we have continued to advance each of these foundational components. In both wake word and ASR, we’ve seen fourfold reductions in recognition errors. In NLU, the error reduction has been threefold — even though the range of utterances that NLU processes, and the range of actions Alexa can take, have both increased dramatically. And in listener studies that use the MUSHRA audio perception methodology, we’ve seen an 80% reduction in the naturalness gap between Alexa’s speech and human speech.

Our overarching strategy for Alexa’s AI has been to combine machine learning (ML) — in particular, deep learning — with the large-scale data and computational resources available through AWS. But these performance improvements are the result of research on a variety of specific topics that extend deep learning, including

  • semi-supervised learning, or using a combination of unlabeled and labeled data to improve the ML system;
  • active learning, or the learning strategy where the ML system selects more-informative samples to receive manual labels;
  • large-scale distributed training, or parallelizing ML-based model training for efficient learning on a large corpus; and
  • context-aware modeling, or using a wide variety of information — including the type of device where a request originates, skills the customer uses or has enabled, and past requests — to improve accuracy.

For more coverage of the anniversary of the Echo's launch, see "Alexa, happy birthday" on Amazon's Day One blog.

Customer impact

From Echo’s launch in November 2014 to now, we have gone from zero customer interactions with Alexa to billions per week. Customers now interact with Alexa in 15 language variants and more than 80 countries.

Through the Alexa Voice Service and the Alexa Skills Kit, we have democratized conversational AI. These self-serve APIs and toolkits let developers integrate Alexa into their devices and create custom skills. Alexa is now available on hundreds of different device types. There are more than 85,000 smart-home products that can be controlled with Alexa, from more than 9,500 unique brands, and third-party developers have built more than 100,000 custom skills.

Ongoing research in conversational AI

Alexa’s success doesn’t mean that conversational AI is a solved problem. On the contrary, we’ve just scratched the surface of what’s possible. We’re working hard to make Alexa …

1. More self-learning

Our scientists and engineers are making Alexa smarter faster by reducing reliance on supervised learning (i.e., building ML models on manually labeled data). A few months back, we announced that we’d trained a speech recognition system on a million hours of unlabeled speech using the teacher-student paradigm of deep learning. This technology is now in production for UK English, where it has improved the accuracy of Alexa’s speech recognizers, and we’re working to apply it to all language variants.

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In the teacher-student paradigm of deep learning, a powerful but impractically slow teacher model is trained on a small amount of hand-labeled data, and it in turn annotates a much larger body of unlabeled data to train a leaner, more efficient student model.

This year, we introduced a new self-learning paradigm that enables Alexa to automatically correct ASR and NLU errors without any human annotator in the loop. In this novel approach, we use ML to detect potentially unsatisfactory interactions with Alexa through signals such as the customer’s barging in on (i.e., interrupting) Alexa. Then, a graphical model trained on customers’ paraphrases of their requests automatically revises failing requests into semantically equivalent forms that work.

For example, “play Sirius XM Chill” used to fail, but from customer rephrasing, Alexa has learned that “play Sirius XM Chill” is equivalent to “play Sirius Channel 53” and automatically corrects the failing variant.

Using this implicit learning technique and occasional explicit feedback from customers — e.g., “did you want/mean … ?” — Alexa is now self-correcting millions of defects per week.

2. More natural

In 2015, when the first third-party skills began to appear, customers had to invoke them by name — e.g., “Alexa, ask Lyft to get me a ride to the airport.” However, with tens of thousands of custom skills, it can be difficult to discover skills by voice and remember their names. This is a unique challenge that Alexa faces.

To address this challenge, we have been exploring deep-learning-based name-free skill interaction to make skill discovery and invocation seamless. For several thousands of skills, customers can simply issue a request — “Alexa, get me a ride to the airport” — and Alexa uses information about the customer’s context and interaction history to decide which skill to invoke.

Another way we’ve made interacting with Alexa more natural is by enabling her to handle compound requests, such as “Alexa, turn down the lights and play music”. Among other innovations, this required more efficient techniques for training semantic parsers, which analyze both the structure of a sentence and the meanings of its parts.

Alexa’s responses are also becoming more natural. This year, we began using neural networks for text-to-speech synthesis. This not only results in more-natural-sounding speech but makes it much easier to adapt Alexa’s TTS system to different speaking styles — a newscaster style for reading the news, a DJ style for announcing songs, or even celebrity voices, like Samuel L. Jackson’s.

3. More knowledgeable

Every day, Alexa answers millions of questions that she’s never been asked before, an indication of customers’ growing confidence in Alexa’s question-answering ability.

The core of Alexa’s knowledge base is a knowledge graph, which encodes billions of facts and has grown 20-fold over the past five years. But Alexa also draws information from hundreds of other sources.

And now, customers are helping Alexa learn through Alexa Answers, an online interface that lets people add to Alexa’s knowledge. In a private beta test and the first month of public release, Alexa customers have furnished Alexa Answers with hundreds of thousands of new answers, which have been shared with customers millions of times.

4. More context-aware and proactive

Today, through an optional feature called Hunches, Alexa can learn how you interact with your smart home and suggest actions when she senses that devices such as lights, locks, switches, and plugs are not in the states that you prefer. We are currently expanding the notion of Hunches to include another Alexa feature called Routines. If you set your alarm for 6:00 a.m. every day, for example, and on waking, you immediately ask for the weather, Alexa will suggest creating a Routine that sets the weekday alarm to 6:00 and plays the weather report as soon as the alarm goes off.

Earlier this year, we launched Alexa Guard, a feature that you can activate when you leave the house. If your Echo device detects the sound of a smoke alarm, a carbon monoxide alarm, or glass breaking, Alexa Guard sends you an alert. Guard’s acoustic-event-detection model uses multitask learning, which reduces the amount of labeled data needed for training and makes the model more compact.

This fall, we will begin previewing an extended version of Alexa Guard that recognizes additional sounds associated with activity, such as footsteps, talking, coughing, or doors closing. Customers can also create Routines that include Guard — activating Guard automatically during work hours, for instance.

5. More conversational

Customers want Alexa to do more for them than complete one-shot requests like “Alexa, play Duke Ellington” or “Alexa, what’s the weather?” This year, we have improved Alexa’s ability to carry context from one request to another, the way humans do in conversation.

For instance, if an Alexa customer asks, “When is The Addams Family playing at the Bijou?” and then follows up with the question “Is there a good Mexican restaurant near there?”, Alexa needs to know that “there” refers to the Bijou. Some of our recent work in this area won one of the two best-paper awards at the Association for Computational Linguistics’ Workshop on Natural-Language Processing for Conversational AI. The key idea is to jointly model the salient entities with transformer networks that use a self-attention mechanism.

However, completing complex tasks that require back-and-forth interaction and anticipation of the customer’s latent goals is still a challenging problem. For example, a customer using Alexa to plan a night out would have to use different skills to find a movie, a restaurant near the theater, and a ride-sharing service, coordinating times and locations.

We are currently testing a new deep-learning-based technology, called Alexa Conversations, with a small group of skill developers who are using it to build high-quality multiturn experiences with minimal effort. The developer supplies Alexa Conversations with a set of sample dialogues, and a simulator expands it into 100 times as much data. Alexa Conversations then uses that data to train a bleeding-edge deep-learning model to predict dialogue actions, without the need for a priori hand-authored rules.

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Dialogue management involves tracking the values of "slots", such as time and location, throughout a conversation. Here, blue arrows indicate slots whose values must be updated across conversational turns.

At re:MARS, we demonstrated a new Night Out planning experience that uses Alexa Conversations technology and novel skill-transitioning algorithms to automatically coordinate conversational planning tasks across multiple skills.

We’re also adapting Alexa Conversations technology to the new concierge feature for Ring video doorbells. With this technology, the doorbell can engage in short conversations on your behalf, taking messages or telling a delivery person where to leave a package. We’re working hard to bring both of these experiences to customers.

What will the next five years look like?

Five years ago, it was inconceivable to us that customers would be interacting with Alexa billions of times per week and that developers would, on their own, build 100,000-plus skills. Such adoption is inspiring our teams to invent at an even faster pace, creating novel experiences that will increase utility and further delight our customers.

1. Alexa everywhere

The Echo family of devices and Alexa’s integration into third-party products has made Alexa a part of millions of homes worldwide. We have been working arduously on bringing the convenience of Alexa, which revolutionized daily convenience in homes, to our customers on the go. Echo Buds, Echo Auto, and the Day 1 Editions of Echo Loop and Echo Frames are already demonstrating that Alexa-on-the-go can simplify our lives even further.

With greater portability comes greater risk of slow or lost Internet connections. Echo devices with built-in smart-home hubs already have a hybrid mode, which allows them to do some spoken-language processing when they can’t rely on Alexa’s cloud-based models. This is an important area of ongoing research for us. For instance, we are investigating new techniques for compressing Alexa’s machine learning models so that they can run on-device.

The new on-the-go hardware isn’t the only way that Alexa is becoming more portable. The new Guest Connect experience allows you to log into your Alexa account from any Echo device — even ones you don’t own — and play your music or preferred news.

2. Moving up the AI stack

Alexa’s unparalleled customer and developer adoption provides new challenges for AI research. In particular, to further shift the cognitive load from customers to AI, we must move up the AI stack, from predictions (e.g., extracting customers’ intents) to more contextual reasoning.

One of our goals is to seamlessly connect disparate skills to increase convenience for our customers. Alexa Conversations and the Night Out experience are the first steps in that direction, completing complex tasks across multiple services and skills.

To enable the same kind of interoperability across different AIs, we helped found the Voice Interoperability Initiative, a consortium of dozens of tech companies uniting to promote customer choice by supporting multiple, interoperable voice services on a single device.

Alexa will also make better decisions by factoring in more information about the customer’s context and history. For instance, when a customer asks an Alexa-enabled device in a hotel room “Alexa, what are the pool hours?”, Alexa needs to respond with the hours for the hotel pool and not the community pool.

We are inspired by the success of learning directly from customers through the self-learning techniques I described earlier. This is an important area where we will continue to incorporate new signals, such as vocal frustration with Alexa, and learn from direct and indirect feedback to make Alexa more accurate.

3. Alexa for everyone

As AI systems like Alexa become an indispensable part of our social fabric, bias mitigation and fairness in AI will require even deeper attention. Our goal is for Alexa to work equally well for all our customers. In addition to our own research, we’ve entered into a three-year collaboration with the National Science Foundation to fund research on fairness in AI.

We envision a future where anyone can create conversational-AI systems. With the Alexa Skills Kit and Alexa Voice Service, we made it easy for developers to innovate using Alexa’s AI. Even end users can build personal skills within minutes using Alexa Skill Blueprints.

We are also thrilled with the Alexa Prize competition, which is democratizing conversational AI by letting university students perform state-of-the-art research at scale. University teams are working on the ultimate conversational-AI challenge of creating socialbots that can converse coherently and engagingly for 20 minutes with humans on a range of current events and popular topics”.

The third instance of the challenge is under way, and we are confident that the university teams will continue to push boundaries — perhaps even give their socialbots an original sense of humor, by far one of the hardest AI challenges.

Together with developers and academic researchers, we’ve made great strides in conversational AI. But there’s so much more to be accomplished. While the future is difficult to predict, one thing I am sure of is that the Alexa team will continue to invent on behalf of our customers.

About the Author
Rohit Prasad is VP and head scientist for Alexa AI.

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Are you interested in applying your strong quantitative analysis and big data skills to world-changing problems? Are you interested in driving the development of methods, models and systems for strategy planning, transportation and fulfillment network? If so, then this is the job for you.Our team is responsible for creating core analytics tech capabilities, platforms development and data engineering. We develop scalable analytics applications and research modeling to optimize operation processes. We standardize and optimize data sources and visualization efforts across geographies, builds up and maintains the online BI services and data mart. You will work with professional software development managers, data engineers, scientists, business intelligence engineers and product managers using rigorous quantitative approaches to ensure high quality data tech products for our customers around the world, including India, Australia, Brazil, Mexico, Singapore and Middle East.We are looking for experienced hands-on Manager of Data Science to join us and lead science programs and developments for Amazon global.Amazon is growing rapidly and because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of analytics tech services and applications fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of Amazon’s worldwide fulfillment networks in emerging countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models.Major responsibilities of the team include:· Translating business questions and concerns into specific analytical questions that can be answered with available data using statistical methods.· Apply Statistical and Machine Learning methods to specific business problems and data.· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc.· Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions.· Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds.· Work with engineers to develop efficient data querying and modeling infrastructure.· Manage your own process. Prioritize and execute on high impact projects, triage external requests, and ensure to deliver projects in time.· Utilizing code (Python, R, Scala, etc.) for analyzing data and building statistical models.
US, WA, Seattle
Job summaryAmazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!As a Senior Applied Scientist on this team, you will:Be a technical leader in Machine Learning and drive full life-cycle Machine Learning projects.Lead technical efforts within this team and across other teams.Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.Run A/B experiments, gather data, and perform statistical analysis.Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.Work closely with software engineers to assist in productionizing your ML models.Research new and innovative machine learning approaches.Recruit Applied Scientists to the team and mentor scientists on the team.Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers 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 a 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 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
The GSF (Global Specialty Fulfillment) organization leads the innovation of Amazon’s ultra-fast fulfillment initiatives. We are an Operations org that hires and manages associates for ultra-fast businesses such as online grocery delivery, sub-same day delivery etc. GSFTech sits within GSF with the mission to build world-class automated Science-Tech products that enable ultra-fast delivery speeds for Amazon customers and job market opportunities for Amazon associates. Our key vision is to transform the online experience. We’re growing in scale and volume, by orders of magnitude. We are a team of passionate tech builders who work endlessly to make life better for our associates through amazing, thoughtful, and creative new scheduling experiences. To succeed, we need senior technical leaders to forge a path into the future by building innovative, maintainable, and scalable systems.At Amazon, we are constantly inventing and re-inventing to be the most associate-centric company in the world. To get there, we need exceptionally talented, bright, and driven people. Amazon is one of the most recognizable brand names in the world and we distribute millions of products each year to our loyal customers.We are looking for an Operational Research Scientist who will be driving optimization initiatives, responsible for building models and prototypes for labor planning systems, and will require close collaboration with other scientists on the team that are developing state-of-the-art ML and forecasting algorithms to scale. Common questions include: when to post shifts given changing demand and associate acceptance? How to optimally assign shifts to associates? This team plays a significant role in various stages of the innovation pipeline from identifying business needs, developing new algorithms, prototyping/simulation, to implementation by working closely with colleagues in engineering, product management, operations, retail and finance.As a Senior member of the scientist team, you will play an integral part on our Operations org with the following technical and leadership responsibilities:· Interact with engineering, operations, science and business teams to develop an understanding and domain knowledge of processes, system structures, and business requirements· Apply domain knowledge and business judgment to identify opportunities and quantify the impact aligning research direction to business requirements and make the right judgment on research project prioritization· Develop scalable models to derive optimal or near-optimal solutions to existing and new scheduling challenges· Create prototypes and simulations to test devised solutions· Advocate technical solutions to business stakeholders, engineering teams, as well as executive-level decision makers· Work closely with engineers to integrate prototypes into production system· Create policy evaluation methods to track the actual performance of devised solutions in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features· Mentor and supervise the work of junior scientists on the team for technical development and their career development and growth· Present business cases and document models, analyses, and their results in order to influence important decisions
US, WA, Seattle
The GSF (Global Specialty Fulfillment) organization leads the innovation of Amazon’s ultra-fast fulfillment initiatives. We are an Operations org that hires and manages associates for ultra-fast businesses such as online grocery delivery, sub-same day delivery etc. GSFTech sits within GSF with the mission to build world-class automated Science-Tech products that enable ultra-fast delivery speeds for Amazon customers and job market opportunities for Amazon associates. Our key vision is to transform the online experience. We’re growing in scale and volume, by orders of magnitude. We are a team of passionate tech builders who work endlessly to make life better for our associates through amazing, thoughtful, and creative new scheduling experiences. To succeed, we need senior technical leaders to forge a path into the future by building innovative, maintainable, and scalable systems.At Amazon, we are constantly inventing and re-inventing to be the most associate-centric company in the world. To get there, we need exceptionally talented, bright, and driven people. Amazon is one of the most recognizable brand names in the world and we distribute millions of products each year to our loyal customers.We are looking for a Senior Applied Scientist who will be the science lead for all key ML and forecasting initiatives, responsible for building models and prototypes for labor planning systems, and will require close collaboration with other scientists on the team that are developing state-of-the-art optimization algorithms to scale. This team plays a significant role in various stages of the innovation pipeline from identifying business needs, developing new algorithms, prototyping/simulation, to implementation by working closely with colleagues in engineering, product management, operations, retail and finance.As a Senior member of the scientist team, you will play an integral part on our Operations org with the following technical and leadership responsibilities:· Help the team define the forward looking Science roadmap and vision by helping to identify, disambiguate and seek out new opportunities· Interact with engineering, operations, science and business teams to develop an understanding and domain knowledge of processes, system structures, and business requirements· Apply domain knowledge and business judgment to identify opportunities and quantify the impact aligning research direction to business requirements and make the right judgment on research project prioritization· Develop scalable models to derive optimal or near-optimal solutions to existing and new scheduling challenges· Create prototypes and simulations to test devised solutions· Advocate technical solutions to business stakeholders, engineering teams, as well as executive-level decision makers· Work closely with engineers to integrate prototypes into production system· Create policy evaluation methods to track the actual performance of devised solutions in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features· Mentor and supervise the work of junior scientists on the team for technical development and their career development and growth· Present business cases and document models, analyses, and their results in order to influence important decisions
CA, ON, Toronto
Job summaryAmazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!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 systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with products - with a high relevance bar and strict latency constraints. 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.As an Applied Scientist for the Sponsored Products Detail Page Allocation and Pricing team, you develop systems which make the final decision on which ads to show, where to place them on the page and how many ads to place. This also includes selection of various themes that would appear in detail pages. This is a challenging technical and business problem, which requires us to balance the interests of advertisers, shoppers, and Amazon. You'll develop a data-driven product strategy to define the right quantitative measures of shopper impact, using this to evaluate decisions and opportunities. You'll balance a portfolio of pragmatic and long-term investments to drive long term growth of the ads and retail businesses. You'll develop real-time algorithms to allocate billions of ads per day in advertising auctions.As an Applied Scientist on this team you will:Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.Run A/B experiments, gather data, and perform statistical analysis.Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.Work closely with software engineers to assist in productionizing your ML models.Research new machine learning approaches.Why you love this opportunityAmazon 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 GrowthYou 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
Job summaryDo you want to join the Alexa Artificial Intelligence (AI) team - 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 AI team, which is in charge of improving Alexa user satisfaction through continuous closed-loop self-learning. The team owns the modules that reduce user perceived defects through automatic defect detection and label generation.Key job responsibilitiesYou will be expected to:· Analyze, understand, and model dialogue context based on large scale speech and dialogue data;· Create and innovate deep learning and/or NLP based algorithms for improving accuracy of Alexa's speech recognition and natural language understanding through contextual modeling;· Perform model/data analysis and monitor user-experienced based metrics through online A/B testing;· Research and implement novel deep learning and NLP algorithms and models.A day in the life· Work collaboratively with scientists and developers to design and implement automated, scalable NLP/ML/IR models for accessing and presenting information· Drive scalable solutions from the business, to prototyping, production testing and through engineering directly to production· Drive best practices on the team, deal with ambiguity and competing objectives, and mentor and guide junior members to achieve their career growth potential.About the teamThe Alexa AI team is in charge of improving Alexa user satisfaction through continuous closed-loop self-learning. The team owns the modules that reduce user perceived defects through automatic defect detection and label generation.You will be working alongside a team of experienced deep learning and NLP scientists and engineers to create deep neural network based contextual dialogue modeling on tasks such as speech translation, natural language understanding, etc.
GB, London
Job summaryCome build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching?Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on.As part of the Prime Video Automated Excellence organization, the Automated Reasoning team applies deep and cutting-edge automated reasoning techniques to detect defects automatically in Prime Video’s core systems and device-level code. The tools we build are mission-critical to the software development and release cycle of many Prime Video engineering organizations, and will represent a huge step forward in the sophistication of our approach to automated software quality. Your work on this team will help us address a new dimension of scale our business faces as we deliver our applications on an ever-expanding set of client devices.Key job responsibilitiesYou will have the opportunity to apply your deep knowledge of automated reasoning techniques, such as static analysis, formal verification, symbolic execution, etc., to concrete problems our product and engineering teams face on a daily basis. You will collaborate with team members to design and deliver enterprise-scale systems that will be used by both internal and external customers. You will have the opportunity to analyse and verify code to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. You will help set and continuously evolve a culture of innovation and curiosity that helps us find and solve our customers’ biggest problems.About the teamTo help a growing organization quickly deliver more features to Prime Video customers, Prime Video’s Automated Excellence organization is innovating on behalf of our global software development team consisting of thousands of engineers. We build services and utilities that make developer’s lives easier and more productive, and that help them deliver at higher levels of quality.
US, CA, Sunnyvale
Job summaryThe Amazon Alexa app is a companion to Alexa devices for setup, remote control, and enhanced features. The Alexa app understands a customer’s habits, preferences and delivers a personalized experience to help them manage their day by providing relevant information as customers want it. We believe voice is the most natural user interface for interacting with technology across many domains; we are inventing the future. As voice-enabled technology becomes increasingly advanced, consumers are demanding more from what their voice products can do. We’re looking for Scientists who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history.As an Senior Data Scientist, you will help build a production scaled personalized recommendation and lead the team to build Machine Learning (ML) and Deep Learning (DL) models to help derive business value and new insights through the adoption of Artificial Intelligence (AI).Key job responsibilitiesThe successful candidate will be responsible for distilling user data insights for ML science applications and influence business decision with data-driven approach to increase Alexa mobile engagement and growth. A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions.· Define the long-term development, science and business strategies for the team.· Expertise in the areas of data science, machine learning and statistics.· Translate business needs into advanced analytics and machine learning models and provide strong algorithm and coding execution and delivery of Machine Learning & Artificial Intelligence.· Work closely with the engineers to architect and develop the best technical design and approach.· Being able to dive a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.· Analyze, extract, normalize, and label relevant data.· Work with Engineers to help our customers operationalize models after they are built.A day in the life· Design and review mobile experiments for growth and engagement· Build statistical models and generate data insights to understand mobile growth and retention· Feature engineering to improve ML model performance.· Analyze, extract, normalize, and label relevant data.· Work with Engineers to deploy applications to production· Work with product manager to convert business problems to science problems and define the solutions.About the teamAlexa Mobile Intelligence team is motivated to make Alexa mobile app being the best intelligent assistant and providing personalized relevant features and content by understanding customers' habits, preferences, hence will reach high growth and retention for the app.
US, CA, San Diego
Job summaryAmazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment.Economists at Amazon will be expected to work directly with senior management on key business problems faced in retail, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. Amazon economists will apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.Key job responsibilitiesAt Customer Trust and Partner Support, we work hard to build trust with both Amazon Customers and Selling Partners. The role offers a unique opportunity to work in a fast growing business at an intersection between Customers and Selling Partners. You will work on a central economist team focused on building scalable analytical solutions for shaping the strategies of the business.We are looking for a seasoned Economist who has a deep and broad analytic background, and is passionate about helping business to make data- and science-based decisions to make a real difference. Experience in applied analytics is essential, and you should be familiar with modern tools for data science and business analysis. We seek creative thinkers and practical doers who can think big and who can also roll up sleeves and execute on these big ideas.A day in the lifeAs a senior economist on the team, you will partner with our business and engineering partners to deliver innovative modeling solutions to help Selling partners and Brands to thrive on Amazon and to ensure a smooth and trustworthy experience for our Customers. This is an opportunity for a high-energy individual to bring economics and Machine Learning techniques into real world applications, to build scalable solutions to systematically deliver impacts to millions of Customers and Selling Partners, and to communicate insights to business and leadership.About the teamAt Selling Partner Risk Econ team, Economists and scientists create science- and data-based solutions at scale for evaluating and optimizing programs for safeguarding consumers against potentially risky sellers and products, and for designing mechanisms to encourage good behavior and deter bad behavior of Amazon sellers.
US, CA, Long Beach
Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment.Economists at Amazon will be expected to work directly with our Chief Economists and senior management on key business problems faced in retail, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. Amazon economists will apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.This role will work in Global Shipping, Our vision is to become the carrier of choice for all of our Selling Partners cross-border shipping needs, offering complete set of end to end cross border solutions from key manufacturing hubs to footprint countries supporting business who use Amazon to grow their business globally.The Global Mile Business Development and strategy team are responsible for growing our global shipping product over the coming years. We have Sales and Marketing, analytical, strategy and forecasting functions and work with a large product and tech team, globally.As we expand, the need for robust demand forecasting to aid decision making on asset utilization especially where we know demand will be variable becomes vital. We are investing in enhanced data and improved modelling techniques, and are now in need of an experienced economist to lead and manage our forecasting and scenario planning function.