Why ambient computing needs self-learning

To become the interface for the Internet of things, conversational agents will need to learn on their own. Alexa has already started down that path.

Today at the annual meeting of the ACM Special Interest Group on Information Retrieval (SIGIR), Ruhi Sarikaya, the director of applied science for Alexa AI, delivered a keynote address titled “Intelligent Conversational Agents for Ambient Computing”. This is an edited version of that talk.

For decades, the paradigm of personal computing was a desktop machine. Then came the laptop, and finally mobile devices so small we can hold them in our hands and carry them in our pockets, which felt revolutionary.

All these devices, however, tether you to a screen. For the most part, you need to physically touch them to use them, which does not seem natural or convenient in a number of situations.

So what comes next?

The most likely answer is the Internet of things (IOT) and other intelligent, connected systems and services. What will the interface with the IOT be? Will you need a separate app on your phone for each connected device? Or when you walk into a room, will you simply speak to the device you want to reconfigure?

At Alexa, we’re betting that conversational AI will be the interface for the IOT. And this will mean a shift in our understanding of what conversational AI is.

Related content
Alexa’s chief scientist on how customer-obsessed science is accelerating general intelligence.

In particular, the IOT creates new forms of context for conversational-AI models. By “context”, we mean the set of circumstances and facts that surround a particular event, situation, or entity, which an AI model can exploit to improve its performance.

For instance, context can help resolve ambiguities. Here are some examples of what we mean by context:

  • Device state: If the oven is on, then the question “What is the temperature?” is more likely to refer to oven temperature than it is in other contexts.
  • Device types: If the device has a screen, it’s more likely that “play Hunger Games” refers to the movie than if the device has no screen.
  • Physical/digital activity: If a customer listens only to jazz, “Play music” should elicit a different response than if the customer listens only to hard rock; if the customer always makes coffee after the alarm goes off, that should influence the interpretation of a command like “start brewing”. 

The same type of reasoning applies to other contextual signals, such as time of day, device and user location, environmental changes as measured by sensors, and so on.

Training a conversational agent to factor in so many contextual signals is much more complicated than training it to recognize, say, song titles. Ideally, we would have a substantial number of training examples for every combination of customer, device, and context, but that’s obviously not practical. So how do we scale the training of contextually aware conversational agents?


The answer is self-learning. By self-learning, we mean a framework that enables an autonomous agent to learn from customer-system interactions, system signals, and predictive models.

Related content
Self-learning system uses customers’ rephrased requests as implicit error signals.

Customer-system interactions can provide both implicit feedback and explicit feedback. Alexa already handles both. If a customer interrupts Alexa’s response to a request — a “barge-in”, as we call it — or rephrases the request, that’s implicit feedback. Aggregated across multiple customers, barge-ins and rephrases indicate requests that aren’t being processed correctly.

Customers can also explicitly teach Alexa how to handle particular requests. This can be customer-initiated, as when customers use Alexa’s interactive-teaching capability, or Alexa-initiated, as when Alexa asks, “Did I answer your question?”

The great advantages of self-learning are that it doesn’t require data annotation, so it scales better while protecting customer privacy; it minimizes the time and cost of updating models; and it relies on high-value training data, because customers know best what they mean and want.

We have a few programs targeting different applications of self-learning, including automated generation of ground truth annotations, defect reduction, teachable AI, and determining root causes of failure.

Automated ground truth generation

At Alexa, we have launched a multiyear initiative to shift Alexa’s ML model development from manual-annotation-based to primarily self-learning-based. The challenge we face is to convert customer feedback, which is often binary or low dimensional (yes/no, defect/non-defect), into high-dimensional synthetic labels such as transcriptions and named-entity annotations.

Our approach has two major components: (1) an exploration module and (2) a feedback collection and label generation module. Here’s the architecture of the label generation model:

Label generation model.png
The ground truth generation model converts customer feedback, which is often binary or low dimensional, into high-dimensional synthetic labels.

The input features include the dialogue context (user utterance, Alexa response, previous turns, next turns), categorical features (domain, intent, dialogue status), numerical features (number of tokens, speech recognition and natural-language-understanding confidence scores), and raw audio data. The model consists of a turn-level encoder and a dialogue-level Transformer-based encoder. The turn-level textual encoder is a pretrained RoBERTa model.

We pretrain the model in a self-supervised way, using synthetic contrastive data. For instance, we randomly swap answers from different dialogues as defect samples. After pretraining, the model is trained in a supervised fashion on multiple tasks, using explicit and implicit user feedback.

Related content
Prime Video beats previous state of the art on the MovieNet dataset by 13% with a new model that is 90% smaller and 84% faster.

We evaluate the label generation model on several tasks. Two of these are goal segmentation, or determining which utterances in a dialogue are relevant to the accomplishment of a particular task, and goal evaluation, or determining whether the goal was successfully achieved.

As a baseline for these tasks, we used a set of annotations each of which was produced in a single pass by a single annotator. Our ground truth, for both the model and the baseline, was a set of annotations each of which had been corroborated by three different human annotators.

Our model’s outputs on both tasks were comparable to the human annotators’: our model was slightly more accurate but had a slightly lower F1 score. We can set a higher threshold, exceeding human performance significantly, and still achieve much larger annotation throughput than manual labeling does.

In addition to the goal-related labels, our model also labels utterances according to intent (the action the customer wants performed, such as playing music), slots (the data types the intent operates on, such as song names), and slot-values (the particular values of the slots, such as “Purple Haze”).

As a baseline for slot and intent labeling, we used a RoBERTa-based model that didn’t incorporate contextual information, and we found that our model outperformed it across the board.

Self-learning-based defect reduction

Three years ago, we deployed a self-learning mechanism that automatically corrects defects in Alexa’s interpretation of customer utterances based purely on implicit signals.

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

This mechanism — unlike the ground truth generation module — doesn’t involve retraining Alexa’s natural-language-understanding models. Instead, it overwrites those models’ outputs, to improve their accuracy.

There are two ways to provide rewrites:

  • Precomputed rewriting produces request-rewrite pairs offline and loads them at run time. This process has no latency constraints, so it can use complex models, and during training, it can take advantage of rich offline signals such as user follow-up turns, user rephrases, Alexa responses, and video click-through rate. Its drawback is that at run time, it can’t take advantage of contextual information.
  • Online rewriting leverages contextual information (e.g., previous dialogue turns, dialogue location, times) at run time to produce rewrites. It enables rewriting of long-tail-defect queries, but it must meet latency constraints, and its training can’t take advantage of offline information.

Precomputed rewriting

We’ve experimented with two different approaches to precomputing rewrite pairs, one that uses pretrained BERT models and one that uses absorbing Markov chains.

This slide illustrates the BERT-based approach:

Rephrase detection.png
The contextual rephrase detection model casts rephrase detection as a span prediction problem, predicting the probability that each token is the start or end of a span.

At left is a sample dialogue in which an Alexa customer rephrases a query twice. The second rephrase elicits the correct response, so it’s a good candidate for a rewrite of the initial query. The final query is not a rephrase, and the rephrase extraction model must learn to differentiate rephrases from unrelated queries.

We cast rephrase detection as a span prediction problem, where we predict the probability that each token is the start or end of a span, using the embedding output of the final BERT layer. We also use timestamping to threshold the number of subsequent customer requests that count as rephrase candidates.

We use absorbing Markov chains to extract rewrite pairs from rephrase candidates that recur across a wide range of interactions.

Absorbing Markov chains.png
The probabilities of sequences of rephrases across customer interactions can be encoded in absorbing Markov chains.

A Markov chain models a dynamic system as a sequence of states, each of which has a certain probability of transitioning to any of several other states. An absorbing Markov chain is one that has a final state, with zero probability of transitioning to any other, which is accessible from any other system state.

We use absorbing Markov chains to encode the probabilities that any given rephrase of the same query will follow any other across a range of interactions. Solving the Markov chain gives us the rewrite for any given request that is most likely to be successful.

Online rewriting

Instead of relying on customers’ own rephrasings, the online rewriting mechanism uses retrieval and ranking models to generate rewrites.

Rewrites are based on customers’ habitual usage patterns with the agent. In the example below, for instance, based on the customer’s interaction history, we rewrite the query “What’s the weather in Wilkerson?” as “What’s the weather in Wilkerson, California?” — even though “What’s the weather in Wilkerson, Washington?” is the more common query across interactions.

The model does, however, include a global layer as well as a personal layer, to prevent overindexing on personalized cases (for instance, inferring that a customer who likes the Selena Gomez song “We Don’t Talk Anymore” will also like the song from Encanto “We Don’t Talk about Bruno”) and to enable the model to provide rewrites when the customer’s interaction history provides little or no guidance.

Online rewriting.png
The online rewriting model’s personal layer factors in customer context, while the global prevents overindexing on personalized cases.

The personalized workstream and the global workstream include both retrieval and ranking models:

  • The retrieval model uses a dense-passage-retrieval (DPR) model, which maps texts into a low-dimensional, continuous space, to extract embeddings for both the index and the query. Then it uses some similarity measurement to decide the rewrite score.
  • The ranking model combines fuzzy match (e.g., through a single-encoder structure) with various metadata to make a reranking decision.

We’ve deployed all three of these self-learning approaches — BERT- and Markov-chain-based offline rewriting and online rewriting — and all have made a significant difference in the quality of Alexa customers’ experience.

Related content
With a new machine learning system, Alexa can infer that an initial question implies a subsequent request.

In experiments, we compared the BERT-based offline approach to four baseline models on six machine-annotated and two human-annotated datasets, and it outperformed all baselines across the board, with improvements of as much as 16% to 17% on some of the machine-annotated datasets, while almost doubling the improvement on the human-annotated ones.

The offline approach that uses absorbing Markov chains has rewritten tens of millions of outputs from Alexa’s automatic-speech-recognition models, and it has a win-loss ratio of 8.5:1, meaning that for every one incorrect rewrite, it has 8.5 correct ones.

And finally, in a series of A/B tests of the online rewrite engine, we found that the global rewrite alone reduced the defect rate by 13%, while the addition of the personal rewrite model reduced defects by a further 4%.

Teachable AI

Query rewrites depend on implicit signals from customers, but customers can also explicitly teach Alexa their personal preferences, such as “I’m a Warriors fan” or “I like Italian restaurants.”

Related content
Deep learning and reasoning enable customers to explicitly teach Alexa how to interpret their novel requests.

Alexa’s teachable-AI mechanism can be either customer-initiated or Alexa-initiated. Alexa proactively senses teachable moments — as when, for instance, a customer repeats the same request multiple times or declares Alexa’s response unsatisfactory. And a customer can initiate a guided Q&A with Alexa with a simple cue like “Alexa, learn my preferences.”

In either case, Alexa can use the customer’s preferences to guide the very next customer interaction.

Failure point isolation

Besides recovering from defects through query rewriting, we also want to understand the root cause of failures for defects.

Dialogue assistants like Alexa depend on multiple models that process customer requests in stages. First, a voice trigger (or “wake word”) model determines whether the user is speaking to the assistant. Then an automatic-speech-recognition (ASR) module converts the audio stream into text. This text passes to a natural-language-understanding (NLU) component that determines the user request. An entity recognition model recognizes and resolves entities, and the assistant generates the best possible response using several subsystems. Finally, the text-to-speech (TTS) model renders the response into human-like speech.

For Alexa, part of self-learning is automatically determining, when a failure occurs, which component has failed. An error in an upstream component can propagate through the pipeline, in which case multiple components may fail. Thus, we focus on the first component that fails in a way that is irrecoverable, which we call the “failure point”.

In our initial work on failure point isolation, we recognize five error points as well as a “correct” class (meaning no component failed). The possible failure points are false wake (errors in voice trigger); ASR errors; NLU errors (for example, incorrectly routing “play Harry Potter” to video instead of audiobook); entity resolution and recognition errors; and result errors (for example, playing the wrong Harry Potter movie).

To better illustrate failure point problem, let's examine a multiturn dialogue:

Failure point isolation slide.png
Failure point isolation identifies the earliest point in the processing pipeline at which a failure occurs, and errors that the conversational agent recovers from are not classified as failures.

In the first turn, the customer is trying to open a garage door, and the conversational assistant recognizes the speech incorrectly. The entity resolution model doesn't recover from this error and also fails. Finally, the dialogue assistant fails to perform the correct action. In this case, ASR is the failure point, despite the other models’ subsequent failure.

On the second turn, the customer repeats the request. ASR makes a small error by not recognizing the article "the" in the speech, but the dialogue assistant takes the correct action. We would mark this turn as correct, as the ASR error didn't lead to downstream failure.

The last turn highlights one of the limitations of our method. The user is asking the dialogue assistant to make a sandwich, which dialogue assistants cannot do — yet. All models have worked correctly, but the user is not satisfied. In our work, we do not consider such turns defective.

On average, our best failure point isolation model achieves close to human performance across different categories (>92% vs human). This model uses extended dialogue context, features derived from logs of the assistants (e.g., ASR confidence), and traces of decision-making components (e.g., NLU modules). We outperform humans in result and correct-class detection. ASR, entity resolution, and NLU are in the 90-95% range.

The day when computing fades into the environment, and we walk from room to room casually instructing embedded computing devices how we want them to behave, may still lie in the future. But at Alexa AI, we’re already a long way down that path. And we’re moving farther forward every day.

Related content

GB, Cambridge
Our team undertakes research together with multiple organizations to advance the state-of-the-art in speech technologies. We not only work on giving Alexa, the ground-breaking service that powers Echo, her voice, but we also develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Senior Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language and Video technology. As a Senior Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech and vocal arts synthesis. Position Responsibilities: - Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. - Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. - Research and implement novel ML and statistical approaches to add value to the business. - Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR
US, WA, Seattle
The Amazon Economics Team is hiring Economist Interns. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets to solve real-world business problems. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with future job market placement. Roughly 85% of interns from previous cohorts have converted to full-time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
We are expanding our Global Risk Management & Claims team and insurance program support for Amazon’s growing risk portfolio. This role will partner with our risk managers to develop pricing models, determine rate adequacy, build underwriting and claims dashboards, estimate reserves, and provide other analytical support for financially prudent decision making. As a member of the Global Risk Management team, this role will provide actuarial support for Amazon’s worldwide operation. Key job responsibilities ● Collaborate with risk management and claims team to identify insurance gaps, propose solutions, and measure impacts insurance brings to the business ● Develop pricing mechanisms for new and existing insurance programs utilizing actuarial skills and training in innovative ways ● Build actuarial forecasts and analyses for businesses under rapid growth, including trend studies, loss distribution analysis, ILF development, and industry benchmarks ● Design actual vs expected and other metrics dashboards to assist decision makings in pricing analysis ● Create processes to monitor loss cost and trends ● Propose and implement loss prevention initiatives with impact on insurance pricing in mind ● Advise underwriting decisions with analysis on driver risk profile ● Support insurance cost budgeting activities ● Collaborate with external vendors and other internal analytics teams to extract insurance insight ● Conduct other ad hoc pricing analyses and risk modeling as needed We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | New York, NY, USA | Seattle, WA, USA
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use reduced-form causal analysis and/or structural economic modeling methods to evaluate the impact of policies on employee outcomes, and examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
US, NY, New York
The Amazon SCOT Forecasting team seeks a Senior Applied Scientist to join our team. Our research team conducts research into the theory and application of reinforcement learning. This research is shared in top journals and conferences and has a significant impact on the field. Through our launch of several Deep RL models into production, our work also affects decision making in the real world. Members of our group have varied interests—from the mathematical foundations of reinforcement learning, to language modeling, to maintaining the performance of generative models in the face of copyrights, and more. Recent work has focused on sample efficiency of RL algorithms, treatment effect estimation, and RL agents integrating real-world constraints, as applied in supply chains. Previous publications include: - Linear Reinforcement Learning with Ball Structure Action Space - Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data - A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation - Deep Inventory Management - What are the Statistical Limits of Offline RL with Linear Function Approximation? Working collaboratively with a group of fellow scientists and engineers, you will identify complex problems and develop solutions in the RL space. We encourage collaboration across teammates and their areas of specialty, leading to creative and ambitious projects with the goal of publication and production. Key job responsibilities - Drive collaborative research and creative problem solving - Constructively critique peer research; mentor junior scientists - Create experiments and prototype implementations of new algorithms and techniques - Collaborate with engineering teams to design and implement software built on these new algorithms - Contribute to progress of the Amazon and broader research communities by producing publications We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
US, CA, Virtual Location - California
If you are interested in this position, please apply on Twitch's Career site https://www.twitch.tv/jobs/en/ About Us: Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate and grow their personal interests and passions. We're always live at Twitch. About the Role: As a Data Scientist, Analytics member of the Data Platform - Insights team, you'll provide data analysis and support for platform, service, and operational engineering teams at Twitch, shaping the way success is measured. Defining what questions should be asked and scaling analytics methods and tools to support our growing business. Additionally, you will help support the vision for business analytics, solutions architecture for data related business constructs, as well as tactical execution such as experiment analysis and campaign performance reporting. You are paving the way for high-quality, high-velocity decisions and will report to the Manager, Data Science. For this role, we're looking for an experienced data staff who will oversee data instrumentation, dashboard/report building, metrics reviews, inform team investments, guidance on success/failure metrics and ad-hoc analysis. You will also work with technical and non-technical staff members throughout the company, and your effort will have an impact on hundreds of partners at Twitch You Will: - Work with members of Platforms & Services to guide them towards better decision making from the available data. - Promote data knowledge and insights through managing communications with partners and other teams, collaborate with colleagues to complete data projects and ensure all parties can use the insights to further improve. - Maintain a customer-centric focus while being a domain and product expert through data, develop trust amongst peers, and ensure that the teams and programs have access to data to make decisions - Manage ambiguous problems and adapt tools to answer complicated questions. - Identify the trade-offs between speed and quality of different approaches. - Create analytical frameworks to measure team success by partnering with teams to establish success metrics, create approaches to track the data and troubleshoot errors, measure and evaluate the data to develop a common language for all colleagues to understand these metrics. - Operationalize data processes to provide partners with ad-hoc analysis, automated dashboards, and self-service reporting tools so that everyone gets a good sense of the state of the business Perks: - Medical, Dental, Vision & Disability Insurance - 401(k), Maternity & Parental Leave - Flexible PTO - Commuter Benefits - Amazon Employee Discount - Monthly Contribution & Discounts for Wellness Related Activities & Programs (e.g., gym memberships, off-site massages), -Breakfast, Lunch & Dinner Served Daily - Free Snacks & Beverages We are open to hiring candidates to work out of one of the following locations: Irvine, CA, USA | Seattle, WA, USA | Virtual Location - CA
US, WA, Bellevue
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? Have you also wondered what are different ways that the transportation assets can be used to delight the customer even more. If so, the Amazon transportation Services, Product and Science is for you . We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed Applied Scientist with strong scientific thinking, good software and statistics experience, skills to help manage projects and operations, improve metrics, and develop scalable processes and tools. The primary role of an Applied Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how we operate the middle mile network. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, machine learning , and the ability to use data and research to make changes. This role requires robust skills in research and implementation of scalable products and models . This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, CA, Los Angeles
The Alexa team is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background, to help build industry-leading Speech and Language technology. Key job responsibilities As an Applied Scientist with the Alexa team, 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. About the team The Alexa team has a mission 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. We are open to hiring candidates to work out of one of the following locations: Los Angeles, CA, USA
US, WA, Seattle
Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as an Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. The position is based in Seattle but will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Please visit https://www.amazon.science for more information Key job responsibilities - Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the international seller services domain. - Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. - Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. - Continuously explore and evaluate state-of-the-art NLP techniques and methodologies to improve the accuracy and efficiency of language-related systems. - Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, CA, Palo Alto
We’re working to improve shopping on Amazon using the conversational capabilities of large language models. We are open to hiring candidates to work out of one of the following locations: Palo Alto, CA, USA