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?

Self-learning

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

IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. 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 generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. 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 generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.