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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The AWS Infrastructure Planning group is responsible for planning and coordinating a complex, multi-tier supply chain that delivers capacity for all AWS services. This includes data center setup, equipment purchase, installation and operation of servers with power and cooling, inventory management and other such decisions. We're building a new suite of tools to automate all AWS supply chain planning, with a broad charter that involves inventory optimization, placement, vendor allocation, transition management, lead time predictions, and more. We are responsible for ensuring that the AWS cloud remains elastic for its customers by taking care of all of the back-end complexity, enabling our infrastructure to stay ahead of our rapid growth.As an Applied Scientist you will use your experience to develop new strategies to improve the performance of AWS Infrastructure’s planning systems and networks. Working closely with fellow applied scientists and product managers, you will use your experience in modeling, optimization, and simulation to design novel algorithms and models of new policies, simulate their performance, and evaluate their benefits and impacts to cost, reliability, and speed of our supply chain.We are looking for experience in network and combinatorial optimization, algorithms, data structures, statistics, and/or machine learning. You will have an opportunity to work on large mathematical problems, with large elements of unpredictability. You will write and solve linear and mixed-integer problems to find optimal solutions to build decisions given capacity constraints and the demand distributions. You will also drive process changes that comes with automation and smarter optimization.Key Responsibilities:· Design and develop mathematical, simulation and optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of inventory management, network flow, supply chain optimization, demand planning.· Apply theories of mathematical optimization, including linear programming, combinatorial optimization, integer programming, dynamic programming, network flows and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software.· Prototype these models by using modeling languages such as R, MATLAB, Mosel or in software languages such as Python.· Create, enhance, and maintain technical documentation, and present to other Scientists, Product, and Engineering teams.· Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans· Influence organization's long term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor other Scientists.
US, WA, Seattle
Percolate is Amazon's content optimization system. We work on one of the company's largest scale machine learning systems operating on peta-bytes of data as well as high-performance real-time recommender systems that determine what content to show to every customer who visits Amazon. We develop models to understand customer behavior, content, and the customer's shopping context to create a personalized experience on all of Amazon's major shopping pages. Put your knowledge to work in cutting edge research areas in deep learning, reinforcement learning, natural language understanding, counterfactual learning, or engineering high performance real-time systems, massive offline data processing systems and analytics platforms. You will have the opportunity to have massive impact, as well as work in a high-performing team environment.This role requires a strong ability to Invent and Simplify: you will leverage your deep scientific expertise, strong problem-solving skills and excellent leadership skills to come up with new ways to get things done, research and define the next generation of our content optimization system. When leading this team, you will be required to:· drive scientist roadmap and determine which areas of research to invest· hire and develop the best· effectively communicate complicated machine learnings concepts to multiple partners.· drive big picture innovations with clear roadmaps for intermediate delivery· identify when to leverage existing technology versus innovate a new approach· work closely with partners to identify problems from the customer's perspective· incorporate subject matter expertise from across the company into our machine learning systemsIf you are excited about enabling hundreds of teams to launch new personalization experiments, working on high performance distributed systems, and want to have massive impact on millions of customers, this is the job for you. You are enthusiastic, self-driven with strong business instincts, have clear communication skills, and a track record of delivering great products. If that sounds like you, please talk to us! We are building the next generation of optimization and intent understanding system that powers the biggest internet retailer on earth, and we hope you will join us!
US, VA, Virtual Location - Virginia
AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. We are looking for a passionate and talented Data Scientist who will collaborate with other scientists and engineers to develop computer vision and machine learning methods and algorithms to address real-world customer use-cases. You'll design and run experiments, research new algorithms, and work closely with talented engineers to put your algorithms and models into practice to help solve our customers' most challenging problems.This position requires that the candidate selected be a U.S. citizen and obtain and maintain an active TS/SCI security clearance.The primary responsibilities of this role are to:· Research, design, implement and evaluate novel ML algorithms.· Work on large-scale datasets, creating scalable, robust and accurate machine learning systems in versatile application fields.· Work closely with account team, research scientist teams and product engineering teams to drive model implementations and new algorithms· Interact with customer directly to understand the business problems and aid them in implementation of their ML solutionsHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.We're dedicated to supporting new team members. Our team has a broad mix of experience levels and Amazon tenures, and we’re building an environment that celebrates knowledge sharing and mentorship.Our team also puts a high value on work-life balance. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here, which is why we aren’t focused on how many hours you spend at work or online. Instead, we’re happy to offer a flexible schedule so you can have a more productive and well-balanced life—both in and outside of work.**If you have questions, wish to apply or refer someone contact Josh May directly - joshmy@amazon.com**
US, NY, New York City
Our team vision is simple yet complex: Create 3D models for every object sold on Amazon.We apply a mix of workflows, image processing, computer vision and machine learning techniques. As part of a world-class research team, you will be working with an Amazon-scale dataset, with Amazon-scale computing resources to devise methods to reconstruct 3D objects from a set of sparse visual inputs.For this specific project, we will be trying to render products from all angles without the explicit use of a 3D model, but rather using neural rendering for better quality. The objective is to scale one of the modern neural rendering techniques to cover the entire Amazon catalog. We aim to generate novels views for better informing customers. As customers will use these renderings to make purchase decisions, the rendering fidelity is of utmost importance.We are seeking an Applied Scientist with a background in Machine Learning applied to Computer Vision or Computer Graphics, and practical experience in implementing neural rendering.Imaging Sciences offers a dynamic workplace that is fueled by innovation and passionate collaboration in a highly multidisciplinary team. We take pride in developing cutting-edge technologies and products that are optimized for the best customer experience. Amazon Imaging Science’s vision is to provide the best product imagery in the industry to empower customers. Through technology, we seek to constantly increase the quality of photos, videos, and other visual media, to lower the cost of acquisition, and to create innovative imaging products.To know more about Amazon science, Please visit https://www.amazon.science
US, WA, Seattle
We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve extreme-scale real world delivery challenges, and provide visible benefit to end-users, this is your opportunity.Come work on the Amazon Prime Air team!We're looking for an outstanding engineer who combines strong technical knowledge in computational fluid dynamics (CFD) and conjugate heat transfer (CHT) with practical experience in aircraft/vehicle design.In this role your responsibilities will range from proposing initial cooling solutions to conducting high-fidelity CFD and CHT simulations to verify thermal and aerodynamic performance. The role will include sizing ducts, fans, and heat sinks, as well as determining their placement on the vehicle, simulating both internal and external flow around electronic components, working with experimentalists to validate component temperatures, and documenting aerodynamic impacts of proposed thermal solutions.Export License ControlThis position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf.
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses.If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you.Major responsibilities· · Use machine learning and analytical techniques to create scalable solutions for business problems· · Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· · Design, development, evaluate and deploy innovative and highly scalable models for predictive learning· · Research and implement novel machine learning and statistical approaches· · Work closely with software engineering teams to drive real-time model implementations and new feature creations· · Work closely with business owners and operations staff to optimize various business operations· · Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· · Mentor other scientists and engineers in the use of ML techniques
US, CA, Milpitas
We are a passionate team of doers that apply cutting-edge advances in technology to solve real-world problems and transform our customers’ experiences in ways we can’t even imagine yet. As an Applied Scientist, you will be working with a unique and gifted team developing exciting products for customers and collaborating with cross-functional teams.Responsibilities· Collaborate across functions to , develop and implement algorithms to solve high-impact problems· Evaluate statistical modeling and Machine Learning approaches using historical data· Define requirements and measurement criteria for scientific and machine learning models.· Translate model prototypes into secure, stable, testable, and maintainable production services.· Develop automated approaches towards monitoring model performance and evaluating impact.· Encourage and support knowledge-sharing within team and external groups· Responsible for influencing technical decisions in areas of /modelling that you identify as critical future offerings· Deliver algorithm and ML projects from beginning to end, including understanding the customer needs, aggregating data, exploring data, building & validating predictive models, and deploying completed models.Amazon is looking for an Applied Scientist to join an exciting new project team working to build a completely new, best in class . Our team is fast paced, highly collaborative and is organized like a startup.Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.Our main office is Milpitas Ca but other US-based Amazon centers are ok.
US, VA, Arlington
Are you seeking an environment where you can drive innovation? Do you want to apply machine learning techniques and advanced statistical modeling to solve real world problems to help Amazon reach and delight millions of customers around the world? Do you want to play a crucial role in the future of Amazon?Every time an Amazon customer makes an order they leverage the most advanced and sophisticated supply chain the world has ever seen, Amazon, and its global network comprising of cutting edge software, fulfillment centers, sort centers, delivery stations, airports, customer service centers, physical stores, robots, airplanes, trucks, vans, trucks, world class employees, trusted partners, and more. Conducting a symphony of this scale comes with significant costs.Here in WW Consumer Finance, our data scientists raise the bar on our ability to fulfill promises to customers at greater convenience, speed, and value through:· Developing visual solutions which help Amazonians search, find, compare, and buy goods / services critical to Amazon's operations· Developing production-ready machine learning solutions to drive savings across Amazon's corporate procurement catalog· Working with technical and non-technical customers across every step of data science project life cycle.· Collaborating with our dedicated product, data engineering, and software development teams to create production implementations for large-scale data analysis.· Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business.· Presenting research results to our internal research community.· Leading training and informational sessions on our science and capabilities.Your contributions will be seen and recognized broadly within Amazon, potentially contributing to the Amazon research corpus and patent portfolio.
US, VA, Arlington
Are you seeking an environment where you can drive innovation? Do you want to apply machine learning techniques and advanced statistical modeling to solve real world problems to help Amazon reach and delight millions of customers around the world? Do you want to play a crucial role in the future of Amazon?Every time an Amazon customer makes an order they leverage the most advanced and sophisticated supply chain the world has ever seen, Amazon, and its global network comprising of cutting edge software, fulfillment centers, sort centers, delivery stations, airports, customer service centers, physical stores, robots, airplanes, trucks, vans, trucks, world class employees, trusted partners, and more. Conducting a symphony of this scale comes with significant costs.Here in WW Consumer Finance, our quantitative researchers raise the bar on our ability to fulfill promises to customers at greater convenience, speed, and value through:· Developing models to support the identification of investment opportunities consistent with Amazon strategic priorities· Developing models identifying synergy opportunities and risks in potential transactions· Serving as a subject matter expert on investment lead pipeline and valuation methodologies· Establish the ongoing processes, skill sets, and strategy that will enable Amazon to continue to build out our financial engineering competency, in the face of extremely fast growth and a rapidly changing industry· Working with technical and non-technical customers across every step of data science project life cycle.· Collaborating with our dedicated product, data engineering, and software development teams to create production implementations for large-scale data analysis.· Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business.· Presenting research results to our internal research community.· Leading training and informational sessions on our science and capabilities.Your contributions will be seen and recognized broadly within Amazon, potentially contributing to the Amazon research corpus and patent portfolio.
US, VA, Arlington
Are you seeking an environment where you can drive innovation? Do you want to apply machine learning techniques and advanced statistical modeling to solve real world problems to help Amazon reach and delight millions of customers around the world? Do you want to play a crucial role in the future of Amazon?Every time an Amazon customer makes an order they leverage the most advanced and sophisticated supply chain the world has ever seen, Amazon, and its global network comprising of cutting edge software, fulfillment centers, sort centers, delivery stations, airports, customer service centers, physical stores, robots, airplanes, trucks, vans, trucks, world class employees, trusted partners, and more. Conducting a symphony of this scale comes with significant costs.Here in WW Consumer Finance, our applied scientists raise the bar on our ability to fulfill promises to customers at greater convenience, speed, and value through:· Developing production ready solutions which help Amazonians search, find, compare, and buy goods / services critical to Amazon's operations· Developing production-ready machine learning solutions to improve Amazon's corporate procurement catalog· Working with technical and non-technical customers across every step of data science project life cycle.· Collaborating with our dedicated product, data engineering, and software development teams to create production implementations for large-scale data analysis.· Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business.· Presenting research results to our internal research community.· Leading training and informational sessions on our science and capabilities.Your contributions will be seen and recognized broadly within Amazon, potentially contributing to the Amazon research corpus and patent portfolio.
US, VA, Arlington
Are you seeking an environment where you can drive innovation? Do you want to apply machine learning techniques and advanced statistical modeling to solve real world problems to help Amazon reach and delight millions of customers around the world? Do you want to play a crucial role in the future of Amazon?Every time an Amazon customer makes an order they leverage the most advanced and sophisticated supply chain the world has ever seen, Amazon, and its global network comprising of cutting edge software, fulfillment centers, sort centers, delivery stations, airports, customer service centers, physical stores, robots, airplanes, trucks, vans, trucks, world class employees, trusted partners, and more. Conducting a symphony of this scale comes with significant costs.Here in WW Consumer Finance our applied scientists raise the bar on our ability to fulfill promises to customers at greater convenience, speed, and value through:· Developing production ready solutions which help Amazonians search, find, compare, and buy goods / services critical to Amazon's operations· Developing production-ready machine learning solutions to improve Amazon's corporate procurement catalog· Working with technical and non-technical customers across every step of data science project life cycle.· Collaborating with our dedicated product, data engineering, and software development teams to create production implementations for large-scale data analysis.· Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business.· Presenting research results to our internal research community.· Leading training and informational sessions on our science and capabilities.Your contributions will be seen and recognized broadly within Amazon, potentially contributing to the Amazon research corpus and patent portfolio.
US, WA, Seattle
We are constantly making Alexa the best voice assistant in the world. Amazon’s Alexa cloud service and Echo devices are used every day, by people you know, in and about their homes. The Alexa Monetization team is hiring talented and experienced Sr. Applied Scientists to help building the next generation products for Alexa across multiple channels and domains. We are seeking an experienced, entrepreneurial, big thinker for a confidential new initiative within Alexa. You will be joining a team doing innovative work, making a direct impact to customers, showing measurable success, and building with the latest natural language processing systems. If you are holding out for an opportunity to:Make a huge impact as an individual· Be part of a team of smart and passionate professionals who will challenge you to grow every day· Solve difficult challenges using your expertise in coding elegant and practical solutions· Create applications at a massive scale used by millions of people· Work with machine learning systems to deliver real experiences, not just researchAnd you are experienced with…· Drive applied science (machine learning) projects end-to-end ~ from ideation, analysis, prototyping, development, metrics, and monitoring· Conduct deep analyses on massive user and contextual data sets· Propose viable modeling ideas to advance optimization or efficiency, with supporting argument, data, or, preferably, preliminary results· Design, develop, and maintain scalable, Machine Learning models with automated training, validation, monitoring and reporting· Stay familiar with the field and apply state-of-the-art Machine Learning techniques to NLP and related optimization problems· Produce peer-reviewed scientific paper in top journals and conferencesAnd you constantly look for opportunities to…· Innovate, simplify, reduce waste, and increase efficiencies· Use data to make decisions and validate assumptions· Automate processes otherwise performed by humans· Learn from others and help grow those around you...then we would love to chat!In 2021, we have the opportunity to build new products and features from the ground up and we are looking for strong, bias for action engineering leaders who are not afraid of taking bold bets and trying new things to improve customer experience for Alexa.As part of a new and growing team, you will be iterating on new features and products to help drive innovation and expansion. You will work on cross-functional and cross-domain opportunities; tackle challenging projects aim to accelerate experimentations in Alexa; and build out operating mechanisms and technology to enable novel customer experiences. You will be instrumental in setting the team culture, quality bar, engineering best practices, and norms. Mentoring and growing the team around you will be one of the primary ways you measure your own success. You will have the opportunity to contribute and develop deep expertise in the areas of distributed systems, machine learning, conversational technologies, user interfaces (including voice and natural user interfaces), data storage and data pipelines.This role is exciting for scientists who love to apply startup mindset to their day-to-day, enjoy working cross-functionally to master both business and technology knowledge, and are passionate about building engineering best practices. If you are looking for opportunity to learn, grow and lead, this is the position for you.