Rohit Prasad, vice president and head scientist for Alexa AI, demonstrates interactive teaching by customers, a new Alexa capability announced last fall.

Alexa: The science must go on

Throughout the pandemic, the Alexa team has continued to invent on behalf of our customers.

COVID-19 has cost us precious lives and served a harsh reminder that so much more needs to be done to prepare for unforeseen events. In these difficult times, we have also seen heroic efforts — from frontline health workers working night and day to take care of patients, to rapid development of vaccines, to delivery of groceries and essential items in the safest possible way given the circumstances.

Communication features.gif
Alexa’s communications capabilities are helping families connect with their loved ones during lockdown.

Alexa has also tried to help where it can. We rapidly added skills that provide information about resources for dealing with COVID-19. We donated Echo Shows and Echo Dots to healthcare providers, patients, and assisted-living facilities around the country, and Alexa’s communications capabilities — including new calling features (e.g., group calling), and the new Care Hub — are helping providers coordinate care and families connect with their loved ones during lockdown.

It has been just over a year since our schools closed down and we started working remotely. With our homes turned into offices and classrooms, one of the challenges has been keeping our kids motivated and on-task for remote learning. Skills such as the School Schedule Blueprint are helping parents like me manage their children’s remote learning and keep them excited about the future.

Despite the challenges of the pandemic, the Alexa team has shown incredible adaptability and grit, delivering scientific results that are already making a difference for our customers and will have long-lasting effects. Over the past 12 months, we have made advances in four thematic areas, making Alexa more

  1. natural and conversational: interactions with Alexa should be as free-flowing as interacting with another person, without requiring customers to use strict linguistic constructs to communicate with Alexa’s ever-growing set of skills. 
  2. self-learning and data efficient: Alexa’s intelligence should improve without requiring manually labeled data, and it should strive to learn directly from customers. 
  3. insightful and proactive: Alexa should assist and/or provide useful information to customers by anticipating their needs.
  4. trustworthy: Alexa should have attributes like those we cherish in trustworthy people, such as discretion, fairness, and ethical behavior.

Natural and conversational 

Accurate far-field automatic speech recognition (ASR) is critical for natural interactions with Alexa. We have continued to make advances in this area, and at Interspeech 2020, we presented 12 papers, including improvements in end-to-end ASR using the recurrent-neural-network-transducer (RNN-T) architecture. ASR advances, coupled with improvements in natural-language understanding (NLU), have reduced the worldwide error rate for Alexa by more than 24% in the past 12 months.

DashHashLM.png
One of Alexa Speech’s Interspeech 2020 papers, “Rescore in a flash: compact, cache efficient hashing data structures for n-gram language models”, proposes a new data structure, DashHashLM, for encoding the probabilities of word sequences in language models with a minimal memory footprint.

Customers depend on Alexa’s ability to answer single-shot requests, but to continue to provide new, delightful experiences, we are teaching Alexa to accomplish complex goals that require multiturn dialogues. In February, we announced the general release of Alexa Conversations, a capability that makes it easy for developers to build skills that engage customers in dialogues. The developer simply provides APIs (application programming interfaces), a list of entity types invoked in the skill, and a small set of sample dialogues that illustrate interactions with the skills’ capabilities. 

Alexa Conversations’ deep-learning-based dialogue manager takes care of the rest by predicting numerous alternate ways in which a customer might engage with the skill. Nearly 150 skills — such as iRobot Home and Art Museum — have now been built with Alexa Conversations, with another 100 under way, and our internal teams have launched capabilities such as Alexa Greetings (where Alexa answers the Ring doorbell on behalf of customers) and “what to read” with the same underlying capability.  

Further, to ensure that existing skills built without Alexa Conversations understand customer requests more accurately, we migrated hundreds of skills to deep neural networks (as opposed to conditional random fields). Migrated skills are seeing increases in understanding accuracy of 15% to 23% across locales. 

Alexa’s skills are ever expanding, with over 100,000 skills built worldwide by external developers. As that number has grown, discovering new skills has become a challenge. Even when customers know of a skill, they can have trouble remembering its name or how to interact with it. 

To make skills more discoverable and eliminate the need to say “Alexa, ask <skill X> to do <Y>,” we launched a deep-learning-based capability for routing utterances that do not have explicit mention of a skill’s name to relevant skills. Thousands of skills are now being discovered naturally, and in preview, they received an average of 15% more traffic. At last year’s International Conference on Acoustics, Speech, and Signal Processing (ICASSP), we presented a novel method for automatically labeling training data for Alexa’s skill selection model, which is crucial to improving utterance routing accuracy as the number of skills continues to grow.  

A constituency tree featuring syntactic-distance measures.
To make the prosody of Alexa's speech more natural, the Amazon Text-to-Speech team uses constituency trees to measure the syntactic distance (orange circles) between words of an utterance, a good indicator of where phrasing breaks or prosodic resets should occur.
Credit: Glynis Condon

As we’ve been improving Alexa’s understanding capabilities, our Text-to-Speech (TTS) synthesis team has been working to increase the naturalness of Alexa’s speech. We have developed prosodic models that enable Alexa to vary patterns of intonation and inflection to fit different conversational contexts. 

This is a first milestone on the path to contextual language generation and speech synthesis. Depending on the conversational context and the speaking attributes of the customer, Alexa will vary its response — both the words chosen and the speaking style, including prosody, stress, and intonation. We also made progress in detecting tone of voice, which can be an additional signal for adapting Alexa’s responses.

Humor is a critical element of human-like conversational abilities. However, recognizing humor and generating humorous responses is one of the most challenging tasks in conversational AI. University teams participating in the Alexa Prize socialbot challenge have made significant progress in this area by identifying opportunities to use humor in conversation and selecting humorous phrases and jokes that are contextually appropriate.

One of our teams is identifying humor in product reviews by detecting incongruity between product titles and questions asked by customers. For instance, the question “Does this make espresso?” might be reasonable when applied to a high-end coffee machine, but applied to a Swiss Army knife, it’s probably a joke. 

We live in a multilingual and multicultural world, and this pandemic has made it even more important for us to connect across language barriers. In 2019, we had launched a bilingual version of Alexa — i.e., customers could address the same device in US English or Spanish without asking Alexa to switch languages on every request. However, the Spanish responses from Alexa were in a different voice than the English responses.  

By leveraging advances in neural text-to-speech (much the way we had used multilingual learning techniques to improve language understanding), we taught the original Alexa voice — which was based on English-only recordings — to speak perfectly accented U.S. Spanish. 

To further break down language barriers, in December we launched two-way language translation, which enables Alexa to act as an interpreter for customers speaking different languages. Alexa can now translate on the fly between English and six other languages on the same device.

In September 2020, I had the privilege of demonstrating natural turn-taking (NTT), a new capability that has the potential to make Alexa even more useful and delightful for our customers. With NTT, Alexa uses visual cues, in combination with acoustic and linguistic information, to determine whether a customer is addressing Alexa or other people in the household — even when there is no wake word. Our teams are working hard on bringing NTT to our customers later this year so that Alexa can participate in conversations just like a family member or a friend.  

Self-learning and data-efficient 

In AI, one definition of generalization is the ability to robustly handle novel situations and learn from them with minimal human supervision. Two years back, we introduced the ability for Alexa to automatically correct errors in its understanding without requiring any manual labeling. This self-learning system uses implicit feedback (e.g., when a customer interrupts a response to rephrase a request) to automatically revise Alexa’s handling of requests that fail. This learning method is automatically addressing 15% of defects, as quickly as a few hours after detection; with supervised learning, these defects would have taken weeks to address. 

Diagram depicting example of paraphrase alignment
We won a best-paper award at last year's International Conference on Computational Linguistics for a self-learning system that finds the best mapping from a successful request to an unsuccessful one, then transfers the training labels automatically.
Credit: Glynis Condon

At December 2020’s International Conference on Computational Linguistics, our scientists won a best-paper award for a complementary approach to self-learning. Where the earlier system overwrites the outputs of Alexa’s NLU models, the newer system uses implicit feedback to create automatically labeled training examples for those models. This approach is particularly promising for the long tail of unusually phrased requests, and it can be used in conjunction with the existing self-learning system.

In parallel, we have been inventing methods that enable Alexa to add new capabilities, intents, and concepts with as little manually labeled data as possible — often by generalizing from one task to another. For example, in a paper at last year’s ACL Workshop on NLP for Conversational AI, we demonstrated the value of transfer learning from reading comprehension to other natural-language-processing tasks, resulting in the best published results on few-shot learning for dialogue state tracking in low-data regimes.

Similarly, at this year’s Spoken Language Technology conference, we showed how to combine two existing approaches to few-shot learning — prototypical networks and data augmentation — to quickly and accurately learn new intents.

Human-like conversational abilities require common sense — something that is still elusive for conversational-AI services, despite the massive progress due to deep learning. We received the best-paper award at the Empirical Methods in Natural Language Processing (EMNLP) 2020 Workshop on Deep Learning Inside Out (DeeLIO) for our work on infusing commonsense knowledge graphs explicitly and implicitly into large pre-trained language models to give machines greater social intelligence. We will continue to build on such techniques to make interactions with Alexa more intuitive for our customers, without requiring a large quantity of annotated data. 

In December 2020, we launched a new feature that allows customers to teach Alexa new concepts. For instance, if a customer says, “Alexa, set the living room light to study mode”, Alexa might now respond, “I don't know what study mode is. Can you teach me?” Alexa extracts a definition from the customer’s answer, and when the customer later makes the same request — or a similar request — Alexa responds with the learned action. 

Alexa uses multiple deep-learning-based parsers to enable such explicit teaching. First, Alexa detects spans in requests that it has trouble understanding. Next, it engages in a clarification dialogue to learn the new concept. Thanks to this novel capability, customers are able to customize Alexa for their needs, and Alexa is learning thousands of new concepts in the smart-home domain every day, without any manual labeling. We will continue to build on this success and develop more self-learning techniques to make Alexa more useful and personal for our customers.

Insightful and proactive

Alexa-enabled ambient devices have revolutionized daily convenience, enabling us to get what we need simply by asking for it. However, the utility of these devices and endpoints does not need to be limited to customer-initiated requests. Instead, Alexa should anticipate customer needs and seamlessly assist in meeting those needs. Smart huncheslocation-based reminders, and discovery of routines are a few ways in which Alexa is already helping customers. 

Illustration of Alexa inferring a customer asking about weather at the beach may be planning a beach trip.
In this interaction, Alexa infers that a customer who asks about the weather at the beach may be interested in other information that could be useful for planning a beach trip.
credit: Glynis Condon

Another way for Alexa to be more useful to our customers is to predict customers’ goals that span multiple disparate skills. For instance, if a customer asks, “How long does it take to steep tea?”, Alexa might answer, “Five minutes is a good place to start", then follow up by asking, "Would you like me to set a timer for five minutes?” In 2020, we launched an initial version of Alexa’s ability to anticipate and complete multi-skill goals without any explicit preprogramming.  

While this ability makes the complex seem simple, underneath, it depends on multiple deep-learning models. A “trigger model” decides whether to predict the customer’s goal at all, and if it decides it should, it suggests a skill to handle the predicted goal. But the skills it suggests are identified by another model that relies on information-theoretic analyses of input utterances, together with subsidiary models that assess features such as whether the customer was trying to rephrase a prior command, or whether the direct goal and the latent goal have common entities or values.  

Trustworthy

We have made significant advances in areas that are key to making Alexa more trusted by customers. In the field of privacy-preserving machine learning, for instance, we have been exploring differential privacy, a theoretical framework for evaluating the privacy protections offered by systems that generate aggregate statistics from individuals’ data. 

At the EMNLP 2020 Workshop on Privacy in Natural Language Processing, we presented a paper that proposes a new way to offer metric-differential-privacy assurances by adding so-called elliptical noise to training data for machine learning systems, and at this year’s Conference of the European Chapter of the Association for Computational Linguistics, we’ll present a technique for transforming texts that preserves their semantic content but removes potentially identifying information. Both methods significantly improve on the privacy protections afforded by older approaches while leaving the performance of the resulting systems unchanged.

Elliptical vs. spherical noise.png
A new approach to protecting privacy in machine learning systems that uses elliptical noise (right) rather than the conventional spherical noise (left) to perturb training data significantly improves privacy protections while leaving the performance of the resulting systems unchanged.


We have also made Alexa’s answers to information-centric questions more trustworthy by expanding our knowledge graph and improving our neural semantic parsing and web-based information retrieval. If, however, the sources of information used to produce a knowledge graph encode harmful social biases — even as a matter of historical accident — the knowledge graph may as well. In a pair of papers presented last year, our scientists devised techniques for both identifying and remediating instances of bias in knowledge graphs, to help ensure that those biases don’t leak into Alexa’s answers to questions.

A two-dimensional representation of our method for measuring bias in knowledge graph embeddings.
A two-dimensional representation of the method for measuring bias in knowledge graph embeddings that we presented last year. In each diagram, the blue dots labeled person1 indicate the shift in an embedding as we tune its parameters. The orange arrows represent relation vectors and the orange dots the sums of those vectors and the embeddings. As we shift the gender relation toward maleness, the profession relation shifts away from nurse and closer to doctor, indicating gender bias.
Credit: Glynis Condon

Similarly, the language models that many speech recognition and natural-language-understanding applications depend on are trained on corpora of publicly available texts; if those data reflect biases, so will the resulting models. At the recent ACM Conference on Fairness, Accountability, and Transparency, Alexa AI scientists presented a new data set that can be used to test language models for bias and a new metric for quantitatively evaluating the test results.

Still, we recognize that a lot more needs to be done in AI in the areas of fairness and ethics, and to that end, partnership with universities and other dedicated research organizations can be a force multiplier. As a case in point, our collaboration with the National Science Foundation to accelerate research on fairness in AI recently entered its second year, with a new round of grant recipients named in February 2021.

And in January 2021, we announced the creation of the Center for Secure and Trusted Machine Learning, a collaboration with the University of Southern California that will support USC and Amazon researchers in the development of novel approaches to privacy-preserving ML solutions

Strengthening the research community

I am particularly proud that, despite the effort required to bring all these advances to fruition, our scientists have remained actively engaged with the broader research community in many other areas. To choose just a few examples:

  • In August, we announced the winners of the third instance of the Alexa Prize Grand Challenge to develop conversational-AI systems, or socialbots, and in September, we opened registration for the fourth instance. Earlier this month, we announced another track of research for Alexa Prize called the TaskBot Challenge, in which university teams will compete to develop multimodal agents that assist customers in completing tasks requiring multiple steps and decisions.
  • In September, we announced the creation of the Columbia Center of Artificial Intelligence Technology, a collaboration with Columbia Engineering that will be a hub of research, education, and outreach programs.
  • In October, we launched the DialoGLUE challenge, together with a set of benchmark models, to encourage research on conversational generalizability, or the ability of dialogue agents trained on one task to adapt easily to new tasks.

Come work with us

Amazon is looking for data scientists, research scientists, applied scientists, interns, and more. Check out our careers page to find all of the latest job listings around the world.

We are grateful for the amazing work of our fellow researchers in the medical, pharmaceutical, and biotech communities who have developed COVID-19 vaccines in record time.

Thanks to their scientific contributions, we now have the strong belief that we will prevail against this pandemic. 

I am looking forward to the end of this pandemic and the chance to work even more closely with the Alexa teams and the broader scientific community to make further advances in conversational AI and enrich our customers’ lives. 

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

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Do you enjoy solving complex problems? Are you eager to change the world with data science? At Amazon Taskless, we challenge ourselves with questions like, what if we can verify documentation in seconds instead of days? What if we could quickly automate complex processes which are not well documented? What if we can improve customer retention?By adopting technologies such as machine learning, computer vision (Amazon Rekognition & Textract) and natural language processing(Amazon Lex), Amazon Taskless transforms tedious businesses processing with Intelligent Automation and Robotic Process Automation. We built an identity management system, which simplify compliance across all Amazon businesses including Twitch, Flex, Amazon sellers, Kindle Direct Publishing authors globally.As a Data Scientist, you will work on our Science team and partner closely with other data scientists , data engineers as well as product managers, UX designers, and business partners across Amazon to accurately model and remove tasks from their processes. Outputs from your models will directly improve customer experience across Amazon while delivering cost savings. You will be responsible for building data science prototypes that optimize business processes and innovate for our customers in new ways.You are skeptical. When someone gives you a data source or walks you through their process, you pepper them with questions about, accuracy, coverage, and the need of steps in their process. When you’re told a model can make assumptions, you aggressively try to break those assumptions.You do whatever it takes to add value. You don’t care whether you’re building complex machine learning models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and retain customers.You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team.You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to make the complex simple to understand.You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer experience are constantly up for debate among senior leadership - you will help drive this conversation.
US, WA, Bellevue
Do you enjoy solving complex problems? Are you eager to change the world with data science? At Amazon Taskless, we challenge ourselves with questions like, what if we can verify documentation in seconds instead of days? What if we could quickly automate complex processes which are not well documented? What if we can improve customer retention?By adopting technologies such as machine learning, computer vision (Amazon Rekognition & Textract) and natural language processing(Amazon Lex), Amazon Taskless transforms tedious businesses processing with Intelligent Automation and Robotic Process Automation. We built an identity management system, which simplify compliance across all Amazon businesses including Twitch, Flex, Amazon sellers, Kindle Direct Publishing authors globally.As a Data Scientist, you will work on our Science team and partner closely with other data scientists , data engineers as well as product managers, UX designers, and business partners across Amazon to accurately model and remove tasks from their processes. Outputs from your models will directly improve customer experience across Amazon while delivering cost savings. You will be responsible for building data science prototypes that optimize business processes and innovate for our customers in new ways.You are skeptical. When someone gives you a data source or walks you through their process, you pepper them with questions about, accuracy, coverage, and the need of steps in their process. When you’re told a model can make assumptions, you aggressively try to break those assumptions.You do whatever it takes to add value. You don’t care whether you’re building complex machine learning models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and retain customers.You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team.You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to make the complex simple to understand.You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer experience are constantly up for debate among senior leadership - you will help drive this conversation.
US, WA, Bellevue
Do you enjoy solving complex problems? Are you eager to change the world with data science? At Amazon Taskless, we challenge ourselves with questions like, what if we can verify documentation in seconds instead of days? What if we could quickly automate complex processes which are not well documented? What if we can improve customer retention?By adopting technologies such as machine learning, computer vision (Amazon Rekognition & Textract) and natural language processing(Amazon Lex), Amazon Taskless transforms tedious businesses processing with Intelligent Automation and Robotic Process Automation. We built an identity management system, which simplify compliance across all Amazon businesses including Twitch, Flex, Amazon sellers, Kindle Direct Publishing authors globally.As a Data Scientist, you will work on our Science team and partner closely with other data scientists , data engineers as well as product managers, UX designers, and business partners across Amazon to accurately model and remove tasks from their processes. Outputs from your models will directly improve customer experience across Amazon while delivering cost savings. You will be responsible for building data science prototypes that optimize business processes and innovate for our customers in new ways.You are skeptical. When someone gives you a data source or walks you through their process, you pepper them with questions about, accuracy, coverage, and the need of steps in their process. When you’re told a model can make assumptions, you aggressively try to break those assumptions.You do whatever it takes to add value. You don’t care whether you’re building complex machine learning models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and retain customers.You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team.You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to make the complex simple to understand.You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer experience are constantly up for debate among senior leadership - you will help drive this conversation.
US, WA, Bellevue
At Amazon we're working to be the most customer-centric company on earth. Within the Access Points team, we do this by creating delivery experiences that delight customers, growing our worldwide network of Amazon Hub Lockers and Counters, providing away from home pickup options, and by creating new delivery initiatives that solve the changing needs of our Customers. At any Access Point, customers should expect to return, or redirect their Amazon deliveries. We measure our impact in transportation savings, revenue, and downstream customer acquisition / engagement /purchasing with Amazon.We are looking for an accomplished Manager, Research Science for Amazon Access Point’s worldwide data science team. You will define the research science direction for the team and work with our engineers to create an advanced system solving mathematically complex constraint problems. You will lead the team to own development of novel algorithmic architectures, toward the ultimate goal of accurately predicting customer purchase propensity, demand pattern and optimizing for site selection topology future Access Point locations and eligible products worldwide.Access Point has a rapidly growing customer base and an exciting science charter in front of us that includes solving highly complex algorithmic problems. You will work closely with and learn from data professionals from various disciplines (eg data engineers, analysts, machine learning engineers, economists and other fellow research scientists).Key responsibilities:· Hire, manager and grow a team of scientists and be the thought leader on the team· Collaborate with product managers and engineering teams to design and implement software solutions for Amazon problems· Contribute to progress of the Amazon and broader research communities by producing publications· Be hands-on when needed, to mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve forecast accuracy or optimization performance
US, PA, Pittsburgh
Amazon is looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading Speech and Language technology. Our mission is to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers.As a 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.We are hiring in all areas of spoken language understanding: ASR, NLU, text-to-speech (TTS), and Dialog Management.Position Responsibilities:· Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications.· Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering.· Routinely build and deploy ML models on available data.· Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists.
CA, ON, Toronto
Amazon Sponsored Ads is one of the fastest growing business domains and we are looking for talented scientists to join this team of incredible scientists to contribute to this growth. We are still in Day 1 and there is an abundance of opportunities that are yet to be explored. We are a team of highly motivated and collaborative team of machine learning and data scientists, with an entrepreneurial spirit and bias for action. We have a broad mandate to experiment and innovate, and we are growing at an unprecedented rate with a seemingly endless range of new opportunities. Sponsored Products (SP) Bids and Budgets team is focussed on helping advertisers set their campaign bids and budgets in an optimized fashion.As an Research Scientist on this team you will:· Build machine learning models and utilize data analysis to deliver scalable solutions to business problems.· Perform hands-on analysis and modeling with very large data sets to develop insights that increase traffic monetization and merchandise sales without compromising shopper experience.· Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production.· Design and run A/B experiments that affect hundreds of millions of customers, evaluate the impact of your optimizations and communicate your results to various business stakeholders.· Work with scientists and economists to model the interaction between organic sales and sponsored content and to further evolve Amazon's marketplace.· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.· Research new predictive learning approaches for the sponsored products business.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, MA, Cambridge
Alexa is Amazon’s intelligent cloud-based voice recognition and natural language understanding virtual assistant. We’re building the speech and language solutions behind Amazon Alexa and other Amazon products and services. Come join our team and help improve the customer experience for the growing base of Alexa users!The Alexa Artificial Intelligence (AI) team is seeking a talented Applied Scientist to build ML models to detect issues that end-users have in their interactions with Alexa (defects and their possible root causes). These models are then used to monitor trends over time with Customer Experience (CX) metrics, guardrail metrics in weblabs, setting defect reduction goals, and defect discovery and resolution.A day in the life· Design, build, test and release predictive ML models· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, and transformation.· Collaborate with colleagues from science, engineering and business backgrounds.· Present proposals and results to partner teams in a clear manner backed by data and coupled with actionable conclusions· Work with engineers to develop efficient data querying and inference infrastructure for both offline and online use casesAbout the hiring groupAlexa AI is an analytics and science team within Alexa. Our mission is to provide an understanding of the customer experience that allows Alexa teams to improve system performance and customer engagement. Our primary deliverables are CX metrics, analytics tools, and customer insights.Job responsibilitiesAs an Applied Scientist with our Alexa AI team, you will work on assessing Alexa's performance using predictive ML models. You will build and improve models to classify Alexa’s responses as correct/incorrect, and predict the most likely cause of failure in cases of incorrect action. Your work will directly impact our customers in the form of products and services that make use of speech and language technology, particularly in developing predictive models to continuously improve the Alexa experience for our customers.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
LU, Luxembourg
Are you a talented and inventive engineer with strong passion about Artificial Intelligence and Predictive Modeling? Would you like to develop Machine-Learning tools by playing a key role within EU RME Predictive Analytics team? Our mission is to drive the Predictive Maintenance (PdM) and Spare Parts (SP) programs for Amazon EU Operations that consists of complex automation, sortation, robotic and materials handling systems.As Machine Learning Tool Specialist you will be working with large distributed systems of data and providing predictive maintenance expertise for over 2000 maintenance engineers, managers and administrators by supporting the entire network managed by EU RME, which may include non-EU locations (such as Singapore, Australia and Japan). You will connect with world leaders in your field and you will be tackling ML challenges by carrying out a systematic review of existing solutions. The appropriate choice of the ML methods and their deployment into effective tools will be the key for the success in this role.The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and outstanding ability in balancing technical leadership with strong business judgment to make the right decisions about model and method choices.Key Areas of Responsibilities:· Provide technical expertise to support team strategies that will take EU RME towards World Class predictive maintenance practices and processes, driving better equipment up-time and lower repair costs with optimized spare parts inventory and placement· Implement an advanced maintenance framework utilizing Machine Learning technologies to drive equipment performance leading to reduced unplanned downtime· Provide technical expertise to support the development of long-term spares management strategies that will ensure spares availability at an optimal level for local sites and reduce the cost of spares
LU, Luxembourg
Are you a talented and inventive engineer with strong passion about Artificial Intelligence and Predictive Modeling? Would you like to develop Machine-Learning tools by playing a key role within EU RME Predictive Analytics team? Our mission is to drive the Predictive Maintenance (PdM) and Spare Parts (SP) programs for Amazon EU Operations that consists of complex automation, sortation, robotic and materials handling systems.As Machine Learning Tool Specialist you will be working with large distributed systems of data and providing predictive maintenance expertise for over 2000 maintenance engineers, managers and administrators by supporting the entire network managed by EU RME, which may include non-EU locations (such as Singapore, Australia and Japan). You will connect with world leaders in your field and you will be tackling ML challenges by carrying out a systematic review of existing solutions. The appropriate choice of the ML methods and their deployment into effective tools will be the key for the success in this role.The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and outstanding ability in balancing technical leadership with strong business judgment to make the right decisions about model and method choices.Key Areas of Responsibilities:· Provide technical expertise to support team strategies that will take EU RME towards World Class predictive maintenance practices and processes, driving better equipment up-time and lower repair costs with optimized spare parts inventory and placement· Implement an advanced maintenance framework utilizing Machine Learning technologies to drive equipment performance leading to reduced unplanned downtime· Provide technical expertise to support the development of long-term spares management strategies that will ensure spares availability at an optimal level for local sites and reduce the cost of spares
US, WA, Seattle
Do you want to join Alexa Artificial Intelligence (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 AI team, which is in charge of improving Alexa user satisfaction through real-time metrics monitoring and continuous closed-loop learning. The team owns the modules that reduce user perceived defects and frictions through utterance reformulation, contextual and personalized hypothesis ranking.A day in the lifeAs a Senior Applied Scientist, you will be working alongside a team of experienced machine/deep learning scientists and engineers to create data driven machine learning models and solutions on tasks such as sequence-to-sequence query reformulation, graph feature embedding, personalized ranking, etc..About the hiring groupThe Alexa AI team is in charge of improving Alexa user satisfaction through real-time metrics monitoring and continuous closed-loop learning. The team owns the modules that reduce user perceived defects and frictions through utterance reformulation, contextual and personalized hypothesis ranking.Job responsibilitiesYou will be expected to:· Analyze, understand, and model user-behavior and the user-experience based on large scale data, to detect key factors causing satisfaction and dissatisfaction (SAT/DSAT).· Build and measure novel online & offline metrics for personal digital assistants and user scenarios, on diverse devices and endpoints· Create and innovate deep learning and/or machine learning based algorithms for utterance reformulation and contextual hypothesis ranking to reduce user dissatisfaction in various scenarios;· Perform model/data analysis and monitor user-experienced based metrics through online A/B testing;· Research and implement novel machine learning and deep learning algorithms and models.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
The Fresh Food Fast organization is responsible for transforming the online and offline grocery experience for Amazon. We are seeking a senior science leader to define our long-term science vision, build out a high-performing team and deliver business critical scientific models to increase customer engagement, inform long-term investment decisions, and measure how grocery is contributing to Prime and Amazon.A day in the life· You will influence senior leaders (VP+) across business, product, finance, and engineering functions and you will partner closely with central Amazon teams to pioneer new models to measure grocery’s future impact to Prime and Amazon.· You will manage a team of Data Scientists, Economists and BIEs to deliver results on behalf of customersAbout the hiring groupWe’re a team of Product Managers, Data Scientists, Economists and Business Intelligence Engineers focused on deeply understanding how F3 customers engage with physical and online grocery stores in order to enhance their shopping experience, drive engagement and loyalty, and measure their long-term impact to Amazon.Job responsibilitiesYour team will apply complex scientific methods to challenging business problems including, “How can we encourage customers to shop more frequently?”, and “how should we measure the impact of physical store expansion and technology innovation in those stores (e.g. Just Walk Out Technology)?”. You will power through ambiguity, finding the right solutions to problems and influencing others to align with your approach and help drive results. You will mentor and develop scientists to achieve their goals, raising the bar technically and driving scale and efficiencies to better leverage our data and technologies.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
DE, BE, Berlin
Our mission is to build the automated intelligence supporting critical service operations at global scale. The Intelligent Cloud Control Machine Learning (ICCML) team works to automate complex large-scale operations of Amazon’s consumer services by developing data-driven, scalable, and seamless solutions available to customers and ICC partners. We employ machine learning to reduce system and information complexity while improving service reliability. We invent practical approaches within application areas such as anomaly detection, time series analysis, classification, causal inference, and text mining, and we apply the latest and most sound techniques of probabilistic modelling, estimation, deep neural networks, and natural language processing (NLP). Working with us offers exciting challenges where you will grow as an applied scientist and technical leader, combining your scientific and engineering skills to solve complex machine learning problems together with our tech teams around the world.As an Applied Scientist of the ICCML team, you will have the important role of mapping business problems to high-impact solutions. You will turn theoretically sound methods into practically applicable models designed for processing massive volumes of data in large-scale environments. You will define business relevant solutions implemented as end-to-end machine learning functions and data processing pipelines that integrate with our partners production systems. In a fast-paced innovation environment, you will work closely with our Applied Scientists, Machine Learning Engineers, and partners to design machine learning models and experiments at scale. You dive deep into all aspects of the practical machine learning development cycle, encompassing sound use of data pre-processing techniques, analysis, modelling, and validation methods. You master the complex theory under the hood of machine learning and you keep up to date with the latest scientific development in information processing, modelling, and learning methods. You take lead of the scientific and technical work in cross-team collaborations with the ultimate objective of creating a delightful experience for our customers using our services.
IL, Tel Aviv
You: Alexa, I am looking for a new career opportunity, where I could conduct applied research, impact millions of customers, and publish about it in top conferences. What do you suggest?Alexa: The Alexa Shopping team is looking for brilliant applied researchers to help me become the best personal shopping assistant. Do you want to hear more?You: Yes, please!Alexa: As an applied researcher in the Alexa Shopping Research team, you will be responsible for research, design, and implementation of new AI technologies for voice assistants. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will inventing, experimenting with, and launching new features, products and systems. Ideally you have a expertise in at least one of the following fields: Web search & data mining, Machine Learning, Natural Language Processing, Computer Vision, Speech Processing or Artificial Intelligence, with both hands-on experience and publications at top relevant academic venues.