"This technology will be transformative in ways we can barely comprehend"

A judge and some of the finalists from the Alexa Prize Grand Challenge 3 talk about the competition, the role of COVID-19, and the future of socialbots.

Human beings are social creatures, and conversations are what connect us—they enable us to share everything from the prosaic to the profound with the people that matter to us. Living through an era marked by pandemic-induced isolation means many of those conversations have shifted online, but the connection they provide remains essential.

So what happens when you replace one of the human participants in a conversation with a socialbot? What does it mean to have an engaging conversation with an AI assistant? How can that kind of conversation prove to be valuable, and can it provide its own kind of connection?

Application period for next Alexa Prize challenge opens

The Amazon Alexa Prize team encourages all interested teams to apply for the Grand Challenge 4 by 11:59 p.m. PST on October 6, 2020.

The participants in this year’s Alexa Prize contest are driven by those questions. Amazon recently announced that a team from Emory University has won the 2020 Alexa Prize. We talked to that team, along with a judge from this year’s competition, as well as representatives from the other finalist teams at Czech Technical University, Stanford University, University of California, Davis, and University of California, Santa Cruz. We wanted to learn what drives them to participate, how COVID-19 has influenced their work and what they see as the possibilities and challenges for socialbots moving forward.

Alexa Prize Grand Challenge 3 winners share their work | Amazon Science

Q: What inspired you to participate in this year’s competition?

Sarah Fillwock, team leader, Emora, Emory University: We had a group of students who were interested in dialogue system research, some of whom had actually participated in the Alexa Prize in its previous years, and we all knew that the Alexa Prize offers a really unique opportunity for anyone interested in this type of work. It is really exciting to use the Alexa device platform to launch a socialbot, because we are able to get hundreds of conversations a day between our socialbot and human users, which really allows for quick turnaround time when assessing whether or not our hypotheses and strategies are improving the performance of our dialogue system.

Marilyn Walker, faculty advisor, Athena, University of California, Santa Cruz: In our Natural Language and Dialogue Systems lab, our main research focus is dialogue management and language generation. Conversational AI is a very challenging problem, and we felt like we could have a research impact in this area. The field has been developing extremely quickly recently, and the Alexa Prize offers an opportunity to try out cutting-edge technologies in dialogue management and language generation on a large Alexa user population.

Amazon Alexa Prize Finalists 2020
The five Alexa Prize finalist teams: Czech Technical University in Prague; Emory University; Stanford University; the University of California, Davis; and the University of California, Santa Cruz.

Vrindavan (Davan) Harrison, team leader, Athena, UCSC: As academics, our primary focus is on research. This year’s competition aimed at being more research-oriented, allowing the teams to spend more time on developing new ideas.

Kai-Hui Liang, team lead, Gunrock, University of California, Davis: Our experience in last year’s competition motivated us to join again as we realized there is still a large room for improvement. I’m especially interested in how to find topics that engage users the most, including trying different ways to elicit and reason about users’ interests. How can we retrieve content that is relevant and interesting, and make the dialog flow more naturally?

Jan Pichl, team leader, Alquist, Czech Technical University: Since the first year of the Alexa Prize competition, we have been developing Alquist to deliver a wide range of topics with a closer focus on the most popular ones. The first Alquist guided a user through the conversation quite strictly. We learned quickly that we needed to introduce more flexibility and let the user be "in charge". With that in mind, we have been pushing Alquist in that direction. Moreover, we want Alquist to manage dialogue utilizing the knowledge graph, and suggest relevant information based on the previously discussed topics and entities.

Christopher D. Manning, faculty advisor, Chirpy Cardinal, Stanford University: It was our first time doing the Alexa Prize, and the team really hadn’t done advance preparation, so it’s all been a wild ride—by which I mean a lot of work and stress for everyone on the team. But it was super exciting that we were largely able to catch up with other leading teams who have been doing the competition for several years.

Hugh Howey, judge and science fiction author: Artificial intelligence is a passionate interest of mine. As a science fiction author, I have the freedom to write about most anything, but the one topic I keep coming back to is the impact that thinking machines already have on our lives and how that impact will only expand in the future. So any chance to be involved with those doing work and research in the field is a no-brainer for me. I leapt at the chance like a Boston Dynamics dog.

Q: What excites you about the potential of socialbots?

Hugh Howey (Judge): This technology will be transformative in ways we can barely comprehend. Right now, the human/computer interface is a bottleneck. It takes a long time for us to tell our computers what we want them to do, and they'll generally only do that thing the one time and forget what it learned. In the future, more and more of the trivial will be automated. This will free up human capital to tackle larger problems. It will also bring us together by removing language barriers, by helping those with disabilities, and eventually this technology will be available to anyone who needs it.

Jinho D. Choi, faculty advisor, Emory: It has been reported that more than 44 million adults in US have mental health issues such as anxiety or depression. We believe that developing an innovative socialbot that comforts people can really help those with mental health conditions, who are generally afraid of talking to other human beings. You may wonder how artificial intelligence can convey a human emotion such as caring. However, humans have used their own creations, such as arts and music, to comfort themselves. It is our vision to advance AI, the greatest invention of humankind, to help individuals learn more about their inner selves so they can feel more positive about themselves, and have a bigger impact in the world.

Ashwin Paranjape, co-team leader, Stanford: As socialbots become more sophisticated and prevalent, increasing numbers of people are chatting with them regularly. As the name suggests, socialbots have the potential to fulfill social needs, such as chit-chatting about everyday life, or providing support to a person struggling with mental health difficulties. Furthermore, socialbots could become a primary user interface through which we engage with the world—for example, chatting about the news, or discussing a book.

Sarah Fillwock, Emory: Our experience in this competition has really solidified this idea of the potential of socialbots being value to people who need support and are in troubling situations. I think that the most compelling role for socialbots in global challenges is to provide a supportive environment to allow people to express themselves, and explore their feelings with regard to whatever dramatic event is going on. This is especially important for vulnerable populations, such as those who do not have a strong social circle or have reduced social contact with others, prohibiting them from being able to achieve the feeling of being valued and understood.

Q: What are the main challenges to realizing that potential?

Abigail See, co-team leader, Stanford: Currently, socialbots struggle to make sense of long, involved conversations, and this limits their ability to talk about any topic in depth. To do this better, socialbots will need to understand what a particular user wants—not only in terms of discussion topics, but also what kind of conversation they want to have. Another important challenge is to allow users to take more initiative, and drive the conversation themselves. Currently, socialbots tend to take more initiative, to ensure the conversation stays within their capabilities. If we can make our socialbots more flexible, they will be much more useful and engaging to people.

Sarah Fillwock, Emory: One major challenge facing the field of dialogue system research is establishing a best practice for evaluation of the performance of dialogue approaches. There is currently a diverse set of evaluation strategies that the research community uses to determine how well their new dialogue approach performs. Another challenge is that dialogues are more than just a pattern-matching problem. Having a back-and-forth dialogue on any topic with another agent tends to involve planning towards achieving specific goals during the conversation as new information about your speaking partner is revealed. Dialogues also rely a lot on having a foundation of general world knowledge that you use to fully understand the implications of what the other person is saying.

Amazon releases Topical Chat dataset

The text-based collection of more than 235,000 utterances will help support high-quality, repeatable research in the field of dialogue systems.

Marilyn Walker, UCSC: There’s a shortage of large annotated conversational corpora for the task of open-domain conversation. For example, progress in NLU has been supported by large annotated corpora, such as Penn Treebank, however, there are currently no such publicly available corpora for open-domain conversation. Also, a rich model of individual users would enable much more natural conversations, but privacy issues currently make it difficult to build such models.

Hugh Howey (Judge): The challenge will be for our ethics and morality to keep up with our gizmos. We will be far more powerful in the future. I only hope we'll be more responsible as well.

Q: What role has the COVID-19 pandemic played in your work?

Jurik Juraska, team member, UCSC: The most immediate effect the onset of the pandemic had on our socialbot was, of course, that it could not just ignore this new dynamic situation. Our socialbot had to acknowledge this new development, as that was what most people were talking about at that point. We would thus have Athena bring up the topic at the beginning of the conversation, sympathizing with the users' current situation, but avoiding wallowing in the negative aspects of it. In the feedback that some users left, there were a number of expressions of gratitude for the ability to have a fun interaction with a socialbot at a time when direct social interaction with friends and family was greatly restricted.

Kai-Hui Liang, UC Davis: We noticed an evident difference in the way Alexa users reacted to popular topics. For example, before COVID-19, many users gave engaging responses when discussing their favorite sports to watch, their travel experiences, or events they plan to do over the weekend. After the breakout of COVID-19, more users replied saying there’s no sports game to watch or they are not able to travel. Therefore, we adapted our topics to better fit the situation. We added discussion about their life experience during the quarantine (eg. how their diet has changed or if they walk outside daily to stay healthy). We also observed more users having negative feelings potentially due to the quarantine. For instance, some users said they feel lonely and they miss their friends or family. Therefore, we enhanced our comforting module that expresses empathy through active listening.

Abigail See, Stanford: As the pandemic unfolded, we saw in real time how users changed their expectations of our socialbot. Not only did they want our bot to deliver up-to-date information, they also wanted it to show emotional understanding for the situation they were in.

Sarah Fillwock, Emory: When COVID became a significant societal issue, we tried two things: we had an experience-oriented COVID topic where our bot discussed with people how they felt about COVID in a sympathetic and reassuring atmosphere, and we had a fact-oriented COVID topic that gave objective information. What we observed was that people had a much stronger positive reaction to the experience-oriented COVID-19 approach than the fact-oriented COVID-19 approach, and seemed to prefer it when talking. This really gave us some empirical evidence that social agents have a strong potential to be helpful in times of turmoil by giving people a safe and caring space to talk about these major events in their life since people responded positively to our approach at doing this.

Q: Lastly, are there any particular advancements in the fields of NLU, dialogue management, conversational AI, etc., that you find promising?

Jan Pichl, Czech Technical University: It is exciting to see the capabilities of the Transformer-based models these days. They are able to generate large articles or even whole stories that are coherent. However, they demand a lot of computation power during the training phase and even during the runtime. Additionally, it is still challenging to use them in a socialbot when you need to work with constantly changing information about the world.

Abigail See, Stanford: As NLP researchers, we are amazed by the incredible pace of progress in the field. Since the last Alexa Prize in 2018, there have been game-changing advancements, particularly in the use of large pretrained language models to understand and generate language. The Alexa Prize offers a unique opportunity for us to apply these techniques, which so far have mostly been tested only on neat, well-defined tasks, and put them in front of real people, with all the messiness that entails! In particular, we were excited to explore the possibility of using neural generative models to chat with people. As recently as the 2018 Alexa Prize, these models generally performed poorly, and so were not used by any of the finalist teams. However, this year, these systems became an important backbone of our system.

Sarah Fillwock, Emory: The work people have been putting into incorporating common sense knowledge and common sense reasoning into dialogue systems is one of the most interesting directions of the current conversational AI field. A lot of the common sense knowledge we use is not explicitly detailed in any type of data set as people have learned them through physical experience or inference over time, so there isn’t necessarily any convenient way to currently accomplish this goal. There have been a lot of attempts to see how far a language modeling approach to dialogue agents can go, but even using huge dialogue data sets and highly complex models still results in hit-and-miss success at common sense information. I am really looking forward to the dialogue approaches and dialogue resources that more explicitly try to model this type of common sense knowledge.


US, WA, Seattle
Job summaryAre you passionate about conducting measurement research and experiments to assess and evaluate talent? Would you like to see your research in products that will drive key talent management behaviors globally to ensure we are raising the bar on our talent? If so, you should consider joining the CXNS team!Amazon CXNS team is an innovative organization that exists to propel Amazon HR toward being the most scientific HR organization on earth. CNXS mission is to use Science to assist and measurably improve every talent decision made at Amazon. CXNS does this by discovering signals in workforce data, infusing intelligence into Amazon’s talent products, and guiding the broader CXNS team to pursue high-impact opportunities with tangible returns. This multi-disciplinary approach spans capabilities, including: data engineering, reporting and analytics, research and behavioral sciences, and applied sciences such as economics and machine learning.In this role, you will support measurement efforts for Amazon Connections (an innovative program that gives Amazonians a confidential and effective way to give feedback on the workplace to help shape the future of the company and improve the employee experience). You will own the research development strategy to evaluate, diagnose, understand, and surface drivers and moderators for key research streams. These include (but are not limited to) attrition, engagement, productivity, diversity, and Amazon culture. You will deep dive and analyze what research should be conducted and to what end, develop hypotheses that can be tested, and support a larger research program to deliver deeper insights that we can surface to leaders on our platform (short term and long).You will use both quantitative and qualitative data as well as conduct research studies to test your hypotheses. You will use a variety of statistical approaches to model and understand behavior. You will develop algorithms and thresholds to surface personalized results to managers/leaders, and partner with machine learning scientists to build these statistical models into production that scales. You will work with an interdisciplinary team of psychologists, economists, ML scientists, UX researchers, engineers, and product managers to inform and build product features to surface deeper people and business insights for our leaders.What you'll do:· Lead a global research strategy to drive more effective decisions and improve the employee experience across all of Amazon· Execute a scalable global content development and research strategy Amazon-wide· Conduct psychometrics analyses to evaluate integrity and practical application of content· Identify research streams to evaluate how to mitigate or remove sources of measurement error· Partner closely and drive effective collaborations across multi-disciplinary research and product teams· Manage full life cycle of large scale research programs (Develop strategy, gather requirements, execute, and evaluate)This person will possess knowledge of different assessment approaches to evaluate performance, a strong psychometrics background, scientific survey methodology, and computing various content validity analyses.
US, WA, Seattle
Job summaryWW Installments is one of the fastest growing businesses within Amazon and we are looking for an Economist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits Amazon.com. We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way. Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.Economists at Amazon are solving some of the most challenging applied economics questions in the tech sector. Amazon economists apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. Our economists build econometric models using our world class data systems, and apply economic theory to solve business problems in a fast-moving environment. A career at Amazon affords economists the opportunity to work with data of unparalleled quality, apply rigorous applied econometric approaches, and work with some of the most talented applied econometricians in the trade.As the Economist within WW Installments, you will be responsible for building long-term causal inference models and experiments. These analysis represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how objective functions are designed and which inputs are consumed for modeling. You will work across functions including machine learning, business intelligence, data engineering, software development, and finance to induce data driven decisions at every level of the organization.Key job responsibilitiesThis role will be responsible for:• Developing a causal inference and experimentation roadmap for the WW Installments Competitive Pricing team.• Apply expertise in causal and econometric modeling to develop large-scale systems that are deployed across Amazon businesses.• Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.• Lead the project plan from a scientific perspective on product launches including identifying potential risks, key milestones, and paths to mitigate risks• Own key inputs to reports consumed by VPs and Directors across Amazon.• Identifying new opportunities to influence business strategy and product vision using causal inference.• Continually improve the WW Installments experimentation roadmap automating and simplifying whenever possible.• Coordinate support across engineers, scientists, and stakeholders to deliver analytical projects and build proof of concept applications.• Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.
US, WA, Seattle
Job summaryWW Installments is one of the fastest growing businesses within Amazon and we are looking for an Applied Scientist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits Amazon.com. We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way. Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.As an Applied Scientist within WW Installments, you will be responsible for building machine learning models and pipelines with direct customer impact. These models represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how they interact with financing options to make purchases. You will work across functions including data engineering, software development, and business to induce data driven decisions at every level of the organization.Key job responsibilitiesThis role will be responsible for:• Developing production machine learning models and pipelines for the WW Installments Competitive Pricing team that directly impact customers.• Apply expertise in machine learning to develop large-scale production systems that are deployed across Amazon businesses.• Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.• Lead the implementation of production ML from a scientific perspective including identifying potential risks, key milestones, and paths to mitigate risks.• Identifying new opportunities to influence business strategy and product vision using data science and machine learning.• Continually improve the WW Installments ML roadmap automating and simplifying whenever possible.• Coordinate support across engineers, scientists, and stakeholders to deliver ML pipelines, analytics projects, and build proof of concept applications.• Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.
US, WA, Seattle
Job summaryWW Installments is one of the fastest growing businesses within Amazon and we are looking for a Data Scientist to join the team. This group has been entrusted with a massive charter that will impact every customer that visits Amazon.com. We are building the next generation of features and payment products that maximize customer enablement in a simple, transparent, and customer obsessed way. Through these products, we will deliver value directly to Amazon customers improving the shopping experience for hundreds of millions of customers worldwide. Our mission is to delight our customers by building payment experiences and financial services that are trusted, valued, and easy to use from anywhere in any way.As a Data Scientist within WW Installments, you will be responsible for building machine learning models and pipelines with direct customer impact. These models represent a core capability for WW Installments and businesses across Amazon. Your work will directly impact customers by influencing how they interact with financing options to make purchases. You will work across functions including data engineering, software development, and business to induce data driven decisions at every level of the organization.Key job responsibilitiesThis role will be responsible for:• Developing machine learning models and pipelines for the WW Installments Competitive Pricing team.• Apply expertise in machine learning to develop large-scale systems that are deployed across Amazon businesses.• Identify business opportunities, define and execute modeling approach, then deliver outcomes to various Amazon businesses with an Amazon-wide perspective for solutions.• Lead the project plan from a scientific perspective on product launches including identifying potential risks, key milestones, and paths to mitigate risks.• Own key inputs to reports consumed by VPs and Directors across Amazon.• Identifying new opportunities to influence business strategy and product vision using data science and machine learning.• Continually improve the WW Installments ML roadmap automating and simplifying whenever possible.• Coordinate support across engineers, scientists, and stakeholders to deliver ML pipelines, analytics projects, and build proof of concept applications.• Work through significant business and technical ambiguity delivering on analytics roadmap across the team with autonomy.
US, CA, Palo Alto
Job summaryAmazon is the 4th most popular site in the US (http://www.alexa.com/topsites/countries/US). Our product search engine is one of the most heavily used services in the world, indexes billions of products, and serves hundreds of millions of customers world-wide. We are working on a new AI-first initiative to re-architect and reinvent the way we do search through the use of extremely large scale next-generation deep learning techniques. Our goal is to make step function improvements in the use of advanced Machine Learning (ML) on very large scale datasets, specifically through the use of aggressive systems engineering and hardware accelerators. This is a rare opportunity to develop cutting edge ML solutions and apply them to a problem of this magnitude. Some exciting questions that we expect to answer over the next few years include:· Can a focus on compilers and custom hardware help us accelerate model training and reduce hardware costs?· Can combining supervised multi-task training with unsupervised training help us to improve model accuracy?· Can we transfer our knowledge of the customer to every language and every locale ? The Search Science team is looking for a Senior Applied Science Manager to drive roadmap on making large business impact through application of Deep Learning models via close collaboration with partner teams. The team also has a focus on technology solution for deep-learning based embedding generation, sensitive data ingestion and applications, data quality measurement, improvement, data bias identification and reduction to achieve model fairness.Success in this role will require the courage to chart a new course. You will manage your own team to understand all aspects of the customer journey. You and your team will inform other scientists and engineers by providing insights and building models to help improving training data quality and reducing bias. The research focus includes but not limited to Natural Language Processing, recommendation, applications relevant to Amazon buyers, sellers and more. You will be working with cutting edge technologies that enable big data and parallelizable algorithms. You will play an active role in translating business and functional requirements into concrete deliverables and working closely with software development teams to put solutions into production.
US, WA, Seattle
Job summaryAmazon EC2 provides cloud computing which forms the foundation for the majority of AWS services, as well as a large portion of compute use cases for businesses and individuals around the world. A critical factor in the continued success of EC2 is the ability to provide reliable and cost effective computing. The EC2 Fleet Health and Lifecycle (EC2 FHL) organization is responsible for ensuring that the global EC2 server fleet continues to raise the bar for reliability, security, and efficiency. We are looking for seasoned engineering leaders with passion for technology and an entrepreneurial mindset. At Amazon, it is all about working hard, having fun and making history. If you are ready to make history, we want to hear from you!Come join a brand new team, EC2 Health Analytics, under EC2 Foundational Technology, to solve complex cutting-edge problems to power a faster, more robust and performant EC2 of tomorrow. The charter of our team is to improve customer experience on the EC2 fleet by analyzing hundreds of signals and driving next-generation detection and remediation tools. We apply Machine Learning to predict outcomes and optimize decisions that improve customer experience and operational efficiency. As an Applied Scientist in the EC2 Health Analytics team, you will join an industry-leading engineering team solving challenging problems at massive scale.· Build a world-class forecasting platform that scales to handling billions of time series data in real time.· Drive fleet utilization improvement where each 1% means tens of millions of additional free cash flow.· Automate tactical and strategic capacity planning tools to optimize for service availability and infrastructure cost.· Build recommendation algorithms for improving the AWS customer experience.· · Reduce dependence on manual troubleshooting for deep-dives.What you will learn:· State-of-the-art analytics and forecasting methodologies.· Application of machine learning to large-scale data sets.· · Product recommendation algorithms.· Resource management and admission control for the Cloud.· The internals of all AWS services.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, CA, Palo Alto
Job summaryThe Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, the Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide.
US, WA, Bellevue
Job summaryThe primary mission of ADECT Monitoring team is to protect customer trust and improve customer experience with Alexa skills and devices. As part of this role, you will build models to improve customer’s experience on Alexa. The team uses various signals to ensure that customers get delightful experiences. This could be through experience improvements or ensuring that only high quality experiences reach customers. We use a lot of data along with multiple approaches such as machine learning and other algorithmic approaches to solve challenges that customers face interacting with Alexa. The ideal candidate will be an expert in the areas of data science, machine learning and statistics, having hands-on experience with multiple improvement initiatives as well as balancing technical and business judgment to make the right decisions about technology, models and methodologies. This involves building conversation arbitration models, which validate conversation quality and metrics to measure and continuously improve on it. These are some of the challenges that have not been solved in the industry before. The candidate needs experience with data science / business intelligence, analytics, and reporting systems while striving for simplicity, and demonstrating significant creativity and high judgment backed by statistical proof. The candidate is also expected to take these models into production, so they need to have some experience with software systems as well. There will be guidance provided on the software front though.
US, WA, Bellevue
Job summaryThe primary mission of ADECT Monitoring team is to protect customer trust and improve customer experience with Alexa skills and devices. The team uses various signals to ensure that customers get delightful experiences. This could be through experience improvements or ensuring that only high quality experiences reach customers. We use a lot of data along with multiple approaches such as machine learning and other algorithmic approaches to solve challenges that customers face interacting with Alexa. The ideal candidate will be an expert in the areas of data science, machine learning and statistics, having hands-on experience with multiple improvement initiatives as well as balancing technical and business judgment to make the right decisions about technology, models and methodologies. As part of this role, you will build models to improve customer’s experience on Alexa. This involves building conversation arbitration models, which validate conversation quality and metrics to measure and continuously improve on it. These are some of the challenges that have not been solved in the industry before. The candidate needs experience with data science / business intelligence, analytics, and reporting systems while striving for simplicity, and demonstrating significant creativity and high judgment backed by statistical proof. The candidate is also expected to work on ML models to improve customer trust. This role will have an opportunity to convert to an Applied Scientist.
US, CA, Sunnyvale
Job summaryAmazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create?The Role:As a Design Analysis Engineer, you will be responsible for bringing new product designs through to manufacturing. Thermal and structural engineering contributes unique, in-depth technical knowledge to solve complex engineering problems in concert with multi-disciplinary teams including Industrial Design, Hardware Engineering, and Operations.You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will:· Perform analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques· Strong fundamentals in dynamics with emphasis on system dynamics, mechanism analysis (Multi Body Dynamics analysis) and co-simulation· Develop, analyze and test thermal, acoustic and structural solutions; from concept design, feature development, product architecture, through system validation· Support creative developments through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques· Use simulation tools like Abaqus, LS-Dyna, Simpack for analysis and design of products· Validate design modifications using simulation and actual prototypes· Use of programming languages like Python and Matlab for analytical/statistical analyses and automation· Establish noise thresholds for usability and compliance requirements· Determine and validate structural performance under use and test conditions· Have strong knowledge of various materials such as heat spreaders solutions to resolve thermal issues, damping materials for noise and vibration suppression· Use various data acquisition systems with thermocouples, accelerometers, strain gauges and IR cameras· Collaborate as part of the device team to iterate and optimize design parameters of enclosures and structural parts to establish and deliver project performance objectives· Design and execute tests using statistical tools to validate analytical models, identify risks and assess design margins· Create and present analytical and experimental results· Develop and apply design guidelines based on project results