From structured search to learning-to-rank-and-retrieve

Using reinforcement learning improves candidate selection and ranking for search, ad platforms, and recommender systems.

Most modern search applications, ad platforms, and recommender systems share a similar multitier information retrieval (IR) architecture with (at a minimum) a candidate selection or retrieval phase and a candidate ordering or ranking phase. Given a query and a context, the retrieval phase reduces the space of possible candidates from millions, sometimes billions, to (typically) hundreds or less. The ranking phase then fine-tunes the ordering of candidates to be presented to customers. This approach is both flexible and scalable.

Search funnel.png
A typical search funnel, from query understanding to displaying results.

At Amazon Music, we have previously improved our ranking of the top-k candidates by applying learning-to-rank (LTR) models, which learn from customer feedback or actions (clicks, likes, adding to favorites, playback, etc.). We combine input signals from the query, context, customer preferences, and candidate features to train the models.

Related content
Models adapted from information retrieval deal well with noisy GPS input and can leverage map information.

However, these benefits apply only to the candidates selected during the retrieval phase. If the best candidate is not in the candidate set, it doesn’t matter how good our ranking model is; customers will not get what they want.

More recently, we have extended the learning-to-rank approach to include retrieval, in what we are calling learning-to-rank-and-retrieve (LTR&R). Where most existing retrieval models are static (deterministic), learning to retrieve is dynamic and leverages customer feedback.

Consequently, we advocate an approach to learning to retrieve that uses contextual multiarmed bandits, a form of reinforcement learning that optimizes the trade-off between exploring new retrieval strategies and exploiting known ones, in order to minimize “regret”.

In what follows, we review prior approaches to both retrieval and ranking and show how, for all of their success, they still have shortcomings that LTR&R helps address.

Candidate selection strategies

Structured search and query understanding

A common candidate retrieval strategy is full-text search, which indexes free-text documents as bags of words stored in an inverted index using term statistics to generate relevance scores (e.g., the BM25 ranking function). The inverted index maps words to documents containing those words.

Full-text search solves for many search use cases, especially when there is an expectation that the candidates for display (e.g., track titles or artist names) should bear a lexical similarity to the query.

Related content
Applications in product recommendation and natural-language processing demonstrate the approach’s flexibility and ease of use.

We can extend full-text search in a couple of ways. One is to bias the results using some measure of entity quality. For example, we can take the popularity of a music track into account when computing a candidate score such that the more popular of two tracks with identical titles will be more likely to make it into the top page.

We can also extend full-text search by applying it in the context of structured data (often referred to as metadata). For instance, fields in a document might contain entity categories (e.g., product types or topics) or entity attributes (such as brand or color) that a more elaborate scoring function (e.g., Lucene scoring) could take into account.

Structured search (SS) can be effectively combined with query understanding (QU), which maps query tokens to entity categories, attributes, or combinations of the two, later used as retrieval constraints. These methods often use content understanding to extract metadata from free text in order to tag objects or entities with categories and attributes stored as fields, adding structure to the underlying text.

Neural retrieval models

More recently, inspired by advances in representation learning, transformers, and large language models for natural-language processing (NLP), search engineers and scientists have turned their attention to vector search (a.k.a. embedding-based retrieval). Vector search uses deep-learning models to produce dense (e.g., sentence-BERT) as well as sparse (e.g., SPLADE) vector representations, called embeddings, that capture the semantic content of queries, contexts, and entities. These models enable information retrieval through fast k-nearest-neighbor (k-NN) vector similarity searches using exact and approximate nearest-neighbor (ANN) algorithms.

Related content
Thorsten Joachims answers 3 questions about the work that earned him the award.

Vector-and-hybrid (lexical + vector) search yields more relevant results than traditional approaches and runs faster on zero-shot IR models, according to the BEIR benchmark. In recommender systems, customer and session embeddings (as query/context) and entity embeddings are also used to personalize candidates in the retrieval stage. These documents can be further reranked by another LTR neural model in a multistage ranking architecture.

A memory index

Research suggests that users’ actions (e.g., query-click information) are the single most important field for retrieval, serving as a running memory of which entities have worked and which haven’t for a given query/context. In a cold-start scenario, we can even train a model that, when given an input document, generates questions that the document might answer (or, more broadly, queries for which the document might be relevant).

Related content
Amazon scientist’s award-winning paper predates — but later found applications in — the deep-learning revolution.

These predicted questions (or queries) and scores are then appended to the original documents, which are indexed as predicted query-entity (Q2E) scores. Once query-entailed user actions on entities are captured, these computed statistics can replace predicted values, becoming actual Q2E scores that update the memory index used in ranking. As newly encountered queries show up, resulting from hits on other strategies, additional Q2E pairs and corresponding scores will be generated.

Real-world complications

In his article “Throwing needles into haystacks”, Daniel Tunkelang writes,

If you’re interested in a particular song, artist, or genre, your interaction with a search engine should be pretty straightforward. If you can express a simple search intent using words that map directly to structured data, you should reasonably expect the search application to understand what you mean and retrieve results accordingly.

However, as we will show, when building a product that serves millions of customers who express themselves in ways that are particular to their experiences and locales, we cannot reasonably expect queries “to express a search intent using words that map directly to structured data.”

Query processing.png
Processing of the query “tayler love” by a complex QU + SS retrieval system.

Let’s start by unpacking an example. Say we want to process the query “love” in a music search system. Even for a single domain (e.g., music/audio) there are many kinds of entities that could match this query, such as songs, artists, playlists, stations, and even podcasts. For each of these categories there could be hundreds and even thousands of possible candidates matching the keyword “love”. Beyond that, each category has different attributes that can also match the keyword (e.g., “love” maps to the genre “love songs”).

Customers may also expect to see related entities in the search results (e.g., artists related to a song returned). So while in the customer’s mind there is surely a main search intent, expressed via a keyword, there could be many possible mappings or interpretations that should be considered. Each of these has a likelihood of being correct, which would generate series of underlying structured searches, first to identify the possible targeted entities and then to bring along related or derived content.

Related content
Framework improves efficiency, accuracy of applications that search for a handful of solutions in a huge space of candidates.

As we have discovered, the crafting and maintenance of such a system is inherently non-scalable.

There is also the problem of compounding errors due to incorrect query understanding and/or content understanding. Category and attribute assignment to queries and entities, which typically uses a combination of human tagging and ML classification models, could be wrong or even completely missing. Furthermore, assignment values may not be binary. For example, “Taylor Swift” is clearly considered a pop artist, but some of her songs are also categorized as country music, alternative/indie, or indie folk.

Given the centrality of interpretation in selecting candidate results, the ability to learn from interactions with customers is essential to successful retrieval. Search applications based on QU+SS and/or FT search, however, usually use static query plans that cannot incorporate feedback in the retrieval stage.

On the other hand, while deep models show enormous promise, they also require significant investment and seem unlikely to completely replace keyword-based retrieval methods in the foreseeable future.

Learning to retrieve

In a world with infinite resources and no latency constraints, we wouldn’t need a retrieval funnel, and we might prefer to rank all possible candidates. But we don’t live in such a world. The reality is that deciding the right balance between increasing precision, usually by exploiting what we already know works, and increasing recall, by exploring more sources and increasing the number of candidates retrieved, is critical for search, ad platforms, and recommender systems. This is especially true in very dynamic applications such as music search, where context matters and new entities, categories, and attributes get added all the time.

And while it would be terrific if we could identify the single candidate selection strategy that produces an optimal top page for every query/context, in practice this is not achievable. The optimal candidate selection strategy depends on the query/context, but we do not know that dependency a priori. We need to learn to retrieve.

Related content
Two KDD papers demonstrate the power and flexibility of Amazon’s framework for “extreme multilabel ranking”.

One way to try to strike the right explore-exploit trade-off is to implement a multiarmed bandit (MAB) optimization, to learn a policy to select a subset of retrieval strategies (arms) that maximize the sum of stochastic rewards earned through a sequence of searches. That is, the policy should maximize the sum of the likelihoods that the expected results are present in the sets produced by such strategies, as later confirmed by user actions (such as clicking on a link).

The MAB approach uses reinforcement learning (RL) to draw more candidates from strategies that perform well while drawing fewer from underperforming strategies. In particular, for learning-to-retrieve, contextual multiarmed bandit algorithms are ideal, as they are designed to take the query/context features and action features (related to the candidate selection strategy) as input to maximize the reward while keeping healthy rate of exploration to minimize regret.

retrieval ensemble.png
Using reinforcement learning to blend podcast search results from different retrieval strategies.

For example, we expect that embeddings based on language models (i.e., a semantic strategy) will perform better for topic search, while the lexical strategy will be more useful for direct entity search (a.k.a. spearfishing queries).

Query/context features may include query information, such as language, type of query, QU slotting and intent classification, query length, etc.; demographic and profile information about your user; information about the current time, such as day of the week, weekend or not, morning or afternoon, holiday season or not, etc.; and historical (aggregate) data of user behavior, such as what genres of music this user has listened to the most.

Action features may include relevance/similarity scores; historical query-strategy performance and number of results; types of entities retrieved, e.g., newly added, popular, personalized, etc.; and information about the underlying retrieval source, e.g., lexical matching, text/graph embeddings, memory, etc.

The model learns a generalization based on these features and the combination of retrieval strategies that maximizes the reward. Finally, we use the union of results produced by the selected strategies to produce a single candidate list that bubbles up to the ranking layer.

LTR&R.png
Generic learning-to-rank-and-retrieve (LTR&R) architecture.

Summary

In conclusion, using query understanding (when available) and structured search is a good place to start when building search systems. By adding learning-to-rank, you can start to reap the benefits of factoring in customer feedback and improving the system’s quality. However, this is not sufficient to address the hard problems we observe in real-life applications like music search.

As an extension to the common retrieval-and-ranking phases present in the multitier IR architectures used in most search, ads, and recommender systems, we propose a generic learning-to-rank-and-retrieve (LTR&R) system architecture that comprises multiple candidate generators based on different retrieval strategies. Some produce well-known, exploitable results, like those based on our memory index, while others focus more on exploration, producing novel, riskier, or more-unexpected results that can increase the diversity of the feedback and provide counterfactual data.

This feedback cannot be collected by the static (i.e., fully deterministic) retrieval-and-ranking systems used nowadays. We also suggest using ML, and in particular RL, to optimize the selection of the subset of retrieval strategies and the number of candidates drawn from them, to maximize the likelihood of finding the expected result in such sets.

By incorporating customer feedback and using ML for LTR&R we can (1) simplify the search systems and (2) bubble up the best possible candidates for our customers. LTR&R is a promising path to solving both precision-oriented search and broad and ambiguous queries that require more recall and exploration.

Acknowledgments: Chris Chow, Adam Tang, Geetha Aluri, and Boris Lerner

Related content

US, WA, Seattle
Alexa is the Amazon cloud service that powers Echo, the groundbreaking Amazon device designed around your voice. We believe voice is the most natural user interface for interacting with technology across many domains; we are inventing the future. Alexa Audio is responsible for fulfilling customers requests for all types of audio content (Music, Radio, Podcasts, Books, custom sounds) across all Alexa enabled devices. This covers a broad set of experiences including search, browse, recommendations, playback, and devices grouping and controls. We are seeking a talented, self-directed Applied Scientists who would come up with state of the art semantic search and recommendation techniques that work with both voice and visual interfaces. This is a unique opportunity where you will be working on latest technologies including LLMs, and also see it impact customer's lives in meaningful ways. Responsibilities - Apply advance state-of-the-art artificial intelligence techniques and develop algorithms in areas of personalization, voice based dialogue systems and natural language information retrieval. - Design scientifically sound online experiments and offline simulations to study and improve products. - Work closely with talented engineers to create scalable models and put them to production. - Perform statistical analyses on large data sets, identify problems, and propose solutions. - Work with partner science teams to identify collaboration opportunities. Work hard. Have fun. Make history. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
ES, M, Madrid
Amazon's International Technology org in EU (EU INTech) is creating new ways for Amazon customers discovering Amazon catalog through new and innovative Customer experiences. Our vision is to provide the most relevant content and CX for their shopping mission. We are responsible for building the software and machine learning models to surface high quality and relevant content to the Amazon customers worldwide across the site. The team, mainly located in Madrid Technical Hub, London and Luxembourg, comprises Software Developer and ML Engineers, Applied Scientists, Product Managers, Technical Product Managers and UX Designers who are experts on several areas of ranking, computer vision, recommendations systems, Search as well as CX. Are you interested on how the experiences that fuel Catalog and Search are built to scale to customers WW? Are interesting on how we use state of the art AI to generate and provide the most relevant content? Key job responsibilities We are looking for Applied Scientists who are passionate to solve highly ambiguous and challenging problems at global scale. You will be responsible for major science challenges for our team, including working with text to image and image to text state of the art models to scale to enable new Customer Experiences WW. You will design, develop, deliver and support a variety of models in collaboration with a variety of roles and partner teams around the world. You will influence scientific direction and best practices and maintain quality on team deliverables. We are open to hiring candidates to work out of one of the following locations: Madrid, M, ESP
GB, Cambridge
The Amazon Artificial General Intelligence (AGI) team is looking for a passionate, highly skilled and inventive Senior Applied Scientist with strong machine learning background to lead the development and implementation of state-of-the-art ML systems for building large-scale, high-quality conversational assistant systems. Key job responsibilities - Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals - Innovate new methods for contextual knowledge extraction and information representation, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, cpu, latency and quality - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in personal knowledge aggregation, processing and verification - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think Big about the arc of development of conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team A day in the life As a Senior Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR
DE, Berlin
The Amazon Artificial General Intelligence (AGI) team is looking for a passionate, highly skilled and inventive Senior Applied Scientist with strong machine learning background to lead the development and implementation of state-of-the-art ML systems for building large-scale, high-quality conversational assistant systems. Key job responsibilities - Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals - Innovate new methods for contextual knowledge extraction and information representation, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, cpu, latency and quality - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in personal knowledge aggregation, processing and verification - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think Big about the arc of development of conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team A day in the life As a Senior Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. We are open to hiring candidates to work out of one of the following locations: Berlin, DEU
US, WA, Bellevue
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Artificial General Intelligence (AGI) organization where our mission is to create a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Supervised Fine-Tuning (SFT), In-Context Learning (ICL), Learning from Human Feedback (LHF), etc. Your work will directly impact our customers in the form of novel products and services . We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Amazon Web Services (AWS) is building a world-class marketing organization, and we are looking for an experienced Applied Scientist to join the central data and science organization for AWS Marketing. You will lead AWS Measurement, targeting, recommendation, forecasting related AI/ML products and initiatives, and own mechanisms to raise the science and measurement standard. You will work with economists, scientists and engineers within the team, and partner with product and business teams across AWS Marketing to build the next generation marketing measurement, valuation and machine learning capabilities directly leading to improvements in our key performance metrics. A successful candidate has an entrepreneurial spirit and wants to make a big impact on AWS growth. You will develop strong working relationships and thrive in a collaborative team environment. You will work closely with business leaders, scientists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. The ideal candidate will have experience with machine learning models and causal inference. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment. You will work on high-impact, high-visibility products, with your work improving the experience of AWS leads and customers. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities * Lead the design, development, deployment, and innovation of advanced science models in the strategic area of marketing measurement and optimization. * Partner with scientists, economists, engineers, and product leaders to break down complex business problems into science approaches. * Understand and mine the large amount of data, prototype and implement new learning algorithms and prediction techniques to improve long-term causal estimation approaches. * Design, build, and deploy effective and innovative ML solutions to improve components of our ML and causal inference pipelines. * Publish and present your work at internal and external scientific venues in the fields of ML and causal inference. * Influence long-term science initiatives and mentor other scientists across AWS. A day in the life Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Austin, TX, USA | New York City, NY, USA | Seattle, WA, USA
US, CA, Santa Clara
Amazon is looking for world class scientists and engineers to join its AWS AI Labs working within natural language processing. This group is entrusted with developing core data mining, natural language processing, and machine learning solutions for AWS services. At AWS AI Labs you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore conceptually large scale natural language processing solutions. You will interact closely with our customers and with the academic community. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. We are open to hiring candidates to work out of one of the following locations: Santa Clara, CA, USA
IN, KA, Bangalore
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Senior Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. A Senior Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video. We are open to hiring candidates to work out of one of the following locations: Bangalore, KA, IND
IN, KA, Bangalore
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. An Applied Scientist will be working with a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll participate the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video. We are open to hiring candidates to work out of one of the following locations: Bangalore, KA, IND
US, WA, Seattle
Are you a scientist interested in pushing the state of the art in Generative AI, LLMs, LMMs? Are you interested in working on ground-breaking research projects that will lead to great products and scientific publications? Do you wish you had access to large datasets? Answer yes to any of these questions and you’ll fit right in here at Amazon. We are looking for a hands-on researcher, who wants to derive, implement, and test the next generation of Generative AI algorithms in multiple projects ranging from Computer Vision, ML, and NLP. The research we do is innovative, multidisciplinary, and far-reaching. We aim to define, deploy, and publish cutting edge research. In order to achieve our vision, we think big and tackle technology problems that are cutting edge. Where technology does not exist, we will build it. Where it exists we will need to modify it to make it work at Amazon scale. We need members who are passionate and willing to learn. Key job responsibilities - Derive novel computer vision, machine learning, and NLP algorithms. - Define scalable computer vision, machine learning and NLP models. - Invent the next generation of Generative AI models. - Work with large datasets. - Work with software engineering teams to deploy your - Publish your work at top conferences/journals. - Mentor team members. A day in the life We are a team of seasoned scientists. We work on science problems and publish our results at major scientific conferences. We work with multiple other science teams at Amazon. About the team We are a tight-knit group that shares our experiences and help each other succeed. We believe in team work. We love hard problems and like to move fast in a growing and changing environment. We use data to guide our decisions and we always push the technology and process boundaries of what is feasible on behalf of our customers. If that sounds like an environment you like, join us. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA