Screenshot shows a portion of the what should I watch experience
The new What Should I Watch (WSIW) experience, released in mid-September, combines Alexa AI and Fire TV recommendations to turn Alexa into an entertainment expert who provides relevant suggestions with a conversational customer experience.

The science behind the new “Alexa, what should I watch?” Fire TV experience

The phrase launches a feature built to help customers navigate an increasingly complex and diverse world of content.

"What should I watch?"

In an entertainment universe filled with a rapidly expanding catalog of shows across myriad channels and apps, this might be one of the most common questions to pop up in many households. And if you are among those who have trouble keeping up with all the latest shows and pinpointing which ones are worth your time, you are not alone.

In fact, more than half of respondents in a recent survey from the consulting firm Deloitte found it difficult to access content across multiple services, and 49% were frustrated if a service failed to provide them with good recommendations. Viewers find themselves surfing … and surfing. It takes the average smart TV owner 12 minutes to land on a show, according to a 2020 survey by Tivo — and for some viewers that can take up to half an hour.

"It's kind of shocking how much time customers have to spend on finding content instead of just sitting down on the couch and jumping into a TV show or a movie that they really enjoy," said Cosmin Laslau, a technical program manager who works on spoken language understanding as part of the Amazon Alexa Entertainment team. "We wanted to leverage new technology to help solve that problem for customers."

Image shows the new Fire TV Cube, left, the Fire TV Omni QLED Series, middle, and the Alexa Voice Remote Pro, right
The What Should I Watch experience works with many Fire TV devices, including the new Fire TV Cube, left, the Fire TV Omni QLED Series, middle, and the Alexa Voice Remote Pro announced at the 2022 Devices and Services event.

The team did that by launching What Should I Watch (WSIW). The new experience, released in mid-September, combines Alexa AI and Fire TV recommendations to turn Alexa into an entertainment expert who provides relevant suggestions with a conversational customer experience. The experience also works with the new Fire TV Cube, the Fire TV Omni QLED Series, and the Alexa Voice Remote Pro announced at the 2022 Devices and Services event.

“We built WSIW to rapidly experiment with new Alexa technologies and push the envelope on discovery experiences to address the core customer need to find something interesting to watch,” explained Parthasarathi Dutta Sharma, a product manager who helped bring WSIW to customers.

WSIW displays personalized recommendations when customers ask, “Alexa, what should I watch?” or a variant of that phrase. Customers can then customize the recommendations using voice prompts (for example, “just the ones that are free to me”) or by using their remote to select filters on the screen, watch trailers, view additional information (eg genre, ratings), and initiate playback.

Related content
Rohit Prasad on the pathway to generalizable intelligence and what excites him most about his re:MARS keynote.

The experience combines innovation for both Fire TV, with its extensive catalog, search and recommendation features, and the conversational AI that drives Alexa.

"We wanted to layer on these new innovations that have been developed around Alexa Conversations specifically," Laslau said. "We've given customers a broad range of natural ways to interact with Alexa, without being limited to a single utterance."

Since previewing WSIW last fall and beginning beta testing with customers, teams have worked to refine the customer experience.

“We used beta testing to closely observe how customers interacted with WSIW and to validate our core hypotheses on what works for customers,” explained Dutta Sharma. “A prime hypothesis we validated was viewers naturally gravitate to using natural language, with variability in inputs, while interacting with Alexa.”

Related content
Dialogue simulator and conversations-first modeling architecture provide ability for customers to interact with Alexa in a natural and conversational manner.

For example, to customize recommendations, the team found that initially customers might say, “I am in the mood for something funny”. They would then follow that by asking, “Which of these are on Prime Video?” or simply stating, “free to me”. So, the team worked to ensure WSIW could support those types of interactions with Alexa. It proved to be a feature customers responded to enthusiastically.

The team also responded to early feedback by introducing more gradual introductions to autoplay trailers and swapped an intro video on how to use the WSIW feature with on-screen contextual hints.

“Another insight was that customers wanted to be able to view only the titles they were already entitled to — versus those for rent or purchase — so we added a permanent free-to-me filter. Customers routinely call that out as a highlight,” Dutta Sharma said.

Building AI for the entertainment space

The What Should I Watch experience builds upon existing Alexa natural language understanding and automatic speech recognition capabilities.

"But bringing natural conversation to the entertainment domain has its own set of unique challenges," Laslau explained. Maybe a show, like The Boys or The Expanse, is ambiguously named, or a movie starts to trend that wasn't in the catalog a week or two ago. Optimizing the feature required combining core advances in AI around natural, multi-turn conversations with a fast-changing catalog.

"We are making sure those natural conversations are intelligent enough to reflect the very latest of what's happening in entertainment," he said.

The team also worked to ensure a mix of personalization based on your preferences— those British detective series you always gravitate toward — and something new that you might not have seen otherwise.

They did this by customizing Fire TV's existing recommender technology, mixing personalization with popular titles and randomizing subsets of these lists so that viewers encounter fresh ideas each time they turn on the TV.

A flywheel effect on innovation

The deep-learning-based Alexa Conversations makes it far simpler to develop the thousands of potential dialogue turns that a “What Should I Watch?” utterance might generate.

Alexa Conversations comprises three models: entity recognition (identifying Tom Cruise as an actor, for example), action prediction (utilizing the “movie searching” API to find movies), and argument filling (indicating the movies to be those with Tom Cruise).

“Alexa Conversations is designed to reduce the burden on developers, generating variations of dialogue automatically. The team has added several new features recently,” said Jiun-Yu Kao, an applied scientist within the Alexa AI Natural Understanding organization.

The WSIW experience is the first to launch with enhanced understanding of screen context.
Jiun-Yu Kao

Those include conversational Q&A which allow customers to ask broad questions about the recommended titles, such as which movies won an Oscar; a context reset function that allows a user to "start over" with a blank slate; and visual context, which enhances Alexa’s ability to respond correctly when a viewer says something like, "play the one on the left,” referencing what’s on the screen instead of naming the movie title.

“The WSIW experience is the first to launch with enhanced understanding of screen context,” Kao said. “It is also the first to combine all above-listed features for improved customer experience.”

Alexa and Fire TV science, engineering, and product teams collaborated to build the different components of the new feature.

Related content
A behind-the-scenes look at the unique challenges the engineering teams faced, and how they used scientific research to drive fundamental innovation to overcome those challenges.

“What’s super cool is that we are tapping into so many different services in parts of Alexa and Fire TV,” said Carlos Mattoso, a Fire TV software development engineer. “We are using a lot of the domain knowledge and capabilities that Fire TV has built around the recommendation space, for instance. But where we do that, we’re also trying to raise the bar: How can we use the information we’re gleaning from usage of What Should I Watch back into the system so that we have this flywheel that continuously improves?”

Mattoso noted that work with the Alexa team enabled not just suggestions but new in-context commands for Fire TV playback and volume changes, for example, that weren’t previously available.

“For instance, when we were building the first beta, we did not really have a way of initiating playback of a title from within an Alexa skill for Fire TV,” he explained. “So, we worked together with the Alexa Video team to extend the existing capability and then add support for that feature so that we could use it on WSIW.”

Looking ahead

Teams continue to work on making What Should I Watch faster and smarter.

One possibility is for users to explicitly guide Alexa by saying something like, "I'm a big sci-fi fan," or "I don't like horror movies." This type of interaction represents an opportunity for Alexa to adapt to customer engagement preferences, with some preferring to guide the service directly, and others wanting to lean back and take in recommendations.

As collaboration on the experience continues, both Alexa and Fire TV are becoming more capable. That could have a broader effect, particularly for the Alexa skill development community.

“We’re really trying to raise the bar,” Mattoso said, “and the capabilities we develop may eventually benefit third-party skill developers. Those might include improved long-term memory, better context resetting, and better visual context understanding.”

Research areas

Related content

GB, Cambridge
We are looking for a passionate, talented, and resourceful Applied Scientist with background in Natural Language Processing (NLP), Large Language Models (LLMs), Question Answering, Information Retrieval, Reinforcement Learning, or Recommender Systems to invent and build scalable solutions for a state-of-the-art conversational assistant. The ideal candidate should have a robust foundation in machine learning and a keen interest in advancing the field. The ideal candidate would also enjoy operating in dynamic environments, have the self-motivation to take on challenging problems to deliver big customer impact, and move fast to ship solutions and then iterate on user feedback and interactions. Key job responsibilities * Work collaboratively with scientists and developers to design and implement automated, scalable NLP/ML/QA/IR models for accessing and presenting information; * Drive scalable solutions end-to-end from business requirements to prototyping, engineering, production testing to production; * Drive best practices on the team, deal with ambiguity and competing objectives, and mentor and guide junior members to achieve their career growth potential. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR
DE, BE, Berlin
We are looking for a passionate, talented, and resourceful Applied Scientist with background in Natural Language Processing (NLP), Large Language Models (LLMs), Question Answering, Information Retrieval, Reinforcement Learning, or Recommender Systems to invent and build scalable solutions for a state-of-the-art conversational assistant. The ideal candidate should have a robust foundation in machine learning and a keen interest in advancing the field. The ideal candidate would also enjoy operating in dynamic environments, have the self-motivation to take on challenging problems to deliver big customer impact, and move fast to ship solutions and then iterate on user feedback and interactions. Key job responsibilities * Work collaboratively with scientists and developers to design and implement automated, scalable NLP/ML/QA/IR models for accessing and presenting information; * Drive scalable solutions end-to-end from business requirements to prototyping, engineering, production testing to production; * Drive best practices on the team, deal with ambiguity and competing objectives, and mentor and guide junior members to achieve their career growth potential. We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU
US, WA, Bellevue
Are you inspired by invention? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Last Mile Solutions Engineering team. WW AMZL Solutions Engineering team is looking to build out our Simulation team to drive innovation across our Last Mile network. We start with the customer and work backwards in everything we do. If you’re interested in joining a rapidly growing team working to build a unique, solutions advisory group with a relentless focus on the customer, you’ve come to the right place. This is a blue-sky role that gives you a chance to roll up your sleeves and dive into big data sets in order to build simulations and experimentation systems at scale, build optimization algorithms and leverage cutting-edge technologies across Amazon. This is an opportunity to think big about how to solve a challenging problem for the customers. As a Simulation Scientist, you are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future. As a simulation scientist, you will apply cutting edge designs and methodologies for complex use cases across Last Mile network to drive innovation. In addition, you will contribute to the end state vision for simulation and experimentation of future delivery stations at Amazon. Key job responsibilities • Design, develop, and simulate engineering solutions for complex material handling challenges considering human/equipment interactions for the Last Mile network • Lead and coordinate simulation efforts for optimal solutions through equipment specification, material flow, process design, ergonomics, associate experience, operational considerations and site layout • The candidate must have the ability to work with diverse customer groups to solve business problems and provide data solutions that are organized and simple to understand. • Working with technical and non-technical customers to design experiments, simulations, and communicate results • Develop, document and update simulation standards, including standard results reports • Create basic to highly advanced models and run "what-if" scenarios to help drive to optimal solutions • Work closely with internal teams to ensure that every detail is thought through and documented using Standard Operating Procedures and/or structured change control • Work closely with vendors, suppliers and other cross functional teams to come up with innovative solutions • Simultaneously manage multiple projects and tasks while effectively influencing, negotiating, and communicating with internal and external business partners • Conduct post-mortem on simulations, after implementation of new designs, in partnering with Safety and Operations A day in the life If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! Benefits: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan Learn more about our benefits here: https://amazon.jobs/en/internal/benefits/us-benefits-and-stock We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
BR, SP, Sao Paulo
Amazon launched the Generative AI Innovation Center in June 2023 to help AWS customers accelerate innovation and business success with Generative AI (https://press.aboutamazon.com/2023/6/aws-announces- generative -ai -innovation center). This Innovation Center provides opportunities to innovate in a fast-paced organization that contributes to breakthrough projects and technologies that are deployed across devices and the cloud. As a data scientist, you are proficient in designing and developing advanced generative AI solutions to solve diverse customer problems. You'll work with terabytes of text, images, and other types of data to solve real-world problems through Gen AI. You will work closely with account teams and ML strategists to define the use case, and with other ML scientists and engineers on the team to design experiments and find new ways to deliver customer value. The selected person will possess technical and customer-facing skills that will enable you to be part of the AWS technical team within our solution providers ecosystem/environment as well as directly to end customers. You will be able to lead discussion with customer and partner staff and senior management. A day in the life Here at AWS, we embrace our differences. We are committed to promoting our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in more than 190 branches around the world. We have innovative benefit offerings and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced by our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and build trust. About the team Work/life balance Our team highly values work-life balance. It's not about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe that finding the right balance between your personal and professional life is fundamental to lifelong happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own work-life balance. Mentoring and career growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and mandates and are building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one guidance and thorough but gentle code reviews. We care about your career growth and strive to assign projects based on what will help each team member become a more well-rounded engineer and enable them to take on more complex tasks in the future. We are open to hiring candidates to work out of one of the following locations: Sao Paulo, SP, BRA
MX, DIF, Mexico City
Amazon launched the Generative AI Innovation Center (GAIIC) in Jun 2023 to help AWS customers accelerate the use of Generative AI to solve business and operational problems and promote innovation in their organization (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As a Data Science Manager in GAIIC, you'll partner with technology and business teams to build new GenAI solutions that delight our customers. You will be responsible for directing a team of data scientists, deep learning architects, and ML engineers to build generative AI models and pipelines, and deliver state-of-the-art solutions to customer’s business and mission problems. Your team will be working with terabytes of text, images, and other types of data to address real-world problems. The successful candidate will possess both technical and customer-facing skills that will allow them to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. 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. A day in the life A day in the life Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. About the team Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. We are open to hiring candidates to work out of one of the following locations: Mexico City, DIF, MEX
US, CA, Palo Alto
The Amazon Search Mission Understanding (SMU) team is at the forefront of revolutionizing the online shopping experience through the Amazon search page. Our ambition extends beyond facilitating a seamless shopping journey; we are committed to creating the next generation of intelligent shopping assistants. Leveraging cutting-edge Large Language Models (LLMs), we aim to redefine navigation and decision-making in e-commerce by deeply understanding our users' shopping missions, preferences, and goals. By developing responsive and scalable solutions, we not only accomplish the shopping mission but also foster unparalleled trust among our customers. Through our advanced technology, we generate valuable insights, providing a guided navigation system into various search missions, ensuring a comprehensive and holistic shopping experience. Our dedication to continuous improvement through constant measurement and enhancement of the shopper experience is crucial, as we strategically navigate the balance between immediate results and long-term business growth. We are seeking an Applied Scientist who is not just adept in the theoretical aspects of Machine Learning (ML), Artificial Intelligence (AI), and Large Language Models (LLMs) but also possesses a pragmatic, hands-on approach to navigating the complexities of innovation. The ideal candidate will have a profound expertise in developing, deploying, and contributing to the next-generation shopping search engine, including but not limited to Retrieval-Augmented Generation (RAG) models, specifically tailored towards enhancing the Rufus application—an integral part of our mission to revolutionize shopping assistance. You will take the lead in conceptualizing, building, and launching groundbreaking models that significantly improve our understanding of and capabilities in enhancing the search experience. A successful applicant will display a comprehensive skill set across machine learning model development, implementation, and optimization. This includes a strong foundation in data management, software engineering best practices, and a keen awareness of the latest developments in distributed systems technology. We are looking for individuals who are determined, analytically rigorous, passionate about applied sciences, creative, and possess strong logical reasoning abilities. Join the Search Mission Understanding team, a group of pioneering ML scientists and engineers dedicated to building core ML models and developing the infrastructure for model innovation. As part of Amazon Search, you will experience the dynamic, innovative culture of a startup, backed by the extensive resources of Amazon.com (AMZN), a global leader in internet services. Our collaborative, customer-centric work environment spans across our offices in Palo Alto, CA, and Seattle, WA, offering a unique blend of opportunities for professional growth and innovation. Key job responsibilities Collaborate with cross-functional teams to identify requirements for ML model development, focusing on enhancing mission understanding through innovative AI techniques, including retrieval-Augmented Generation or LLM in general. Design and implement scalable ML models capable of processing and analyzing large datasets to improve search and shopping experiences. Must have a strong background in machine learning, AI, or computational sciences. Lead the management and experiments of ML models at scale, applying advanced ML techniques to optimize science solution. Serve as a technical lead and liaison for ML projects, facilitating collaboration across teams and addressing technical challenges. Requires strong leadership and communication skills, with a PhD in Computer Science, Machine Learning, or a related field. We are open to hiring candidates to work out of one of the following locations: Palo Alto, CA, USA | Seattle, WA, USA
US, WA, Seattle
Alexa Personality Fundamentals is chartered with infusing Alexa's trustworthy, reliable, considerate, smart, and playful personality. Come join us in creating the future of personality forward AI here at Alexa. Key job responsibilities As a Data Scientist with Alexa Personality, your work will involve machine learning, Large Language Model (LLM) and other generative technologies. You will partner with engineers, applied scientists, voice designers, and quality assurance to ensure that Alexa can sing, joke, and delight our customers in every interaction. You will take a central role in defining our experimental roadmap, sourcing training data, authoring annotation criteria and building automated benchmarks to track the improvement of our Alexa's personality. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Seattle, WA, USA
US, CA, Palo Alto
The Amazon Search Mission Understanding (SMU) team is at the forefront of revolutionizing the online shopping experience through the Amazon search page. Our ambition extends beyond facilitating a seamless shopping journey; we are committed to creating the next generation of intelligent shopping assistants. Leveraging cutting-edge Large Language Models (LLMs), we aim to redefine navigation and decision-making in e-commerce by deeply understanding our users' shopping missions, preferences, and goals. By developing responsive and scalable solutions, we not only accomplish the shopping mission but also foster unparalleled trust among our customers. Through our advanced technology, we generate valuable insights, providing a guided navigation system into various search missions, ensuring a comprehensive and holistic shopping experience. Our dedication to continuous improvement through constant measurement and enhancement of the shopper experience is crucial, as we strategically navigate the balance between immediate results and long-term business growth. We are seeking an Applied Scientist who is not just adept in the theoretical aspects of Machine Learning (ML), Artificial Intelligence (AI), and Large Language Models (LLMs) but also possesses a pragmatic, hands-on approach to navigating the complexities of innovation. The ideal candidate will have a profound expertise in developing, deploying, and contributing to the next-generation shopping search engine, including but not limited to Retrieval-Augmented Generation (RAG) models, specifically tailored towards enhancing the Rufus application—an integral part of our mission to revolutionize shopping assistance. You will take the lead in conceptualizing, building, and launching groundbreaking models that significantly improve our understanding of and capabilities in enhancing the search experience. A successful applicant will display a comprehensive skill set across machine learning model development, implementation, and optimization. This includes a strong foundation in data management, software engineering best practices, and a keen awareness of the latest developments in distributed systems technology. We are looking for individuals who are determined, analytically rigorous, passionate about applied sciences, creative, and possess strong logical reasoning abilities. Join the Search Mission Understanding team, a group of pioneering ML scientists and engineers dedicated to building core ML models and developing the infrastructure for model innovation. As part of Amazon Search, you will experience the dynamic, innovative culture of a startup, backed by the extensive resources of Amazon.com (AMZN), a global leader in internet services. Our collaborative, customer-centric work environment spans across our offices in Palo Alto, CA, and Seattle, WA, offering a unique blend of opportunities for professional growth and innovation. Key job responsibilities Collaborate with cross-functional teams to identify requirements for ML model development, focusing on enhancing mission understanding through innovative AI techniques, including retrieval-Augmented Generation or LLM in general. Design and implement scalable ML models capable of processing and analyzing large datasets to improve search and shopping experiences. Must have a strong background in machine learning, AI, or computational sciences. Lead the management and experiments of ML models at scale, applying advanced ML techniques to optimize science solution. Serve as a technical lead and liaison for ML projects, facilitating collaboration across teams and addressing technical challenges. Requires strong leadership and communication skills, with a PhD in Computer Science, Machine Learning, or a related field. We are open to hiring candidates to work out of one of the following locations: Palo Alto, CA, USA | Seattle, WA, USA
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Science Manager with a strong deep learning background, to lead the development of industry-leading technology with multimodal systems. Key job responsibilities As an Applied Science Manager with the AGI team, you will lead the development of novel algorithms and modeling techniques to advance the state of the art with multimodal systems. Your work will directly impact our customers in the form of products and services that make use of vision and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development with multimodal Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) in Computer Vision. About the team The AGI team has a mission to push the envelope with multimodal LLMs and GenAI in Computer Vision, 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: Bellevue, WA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
US, WA, Seattle
Amazon is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong machine learning background to help build industry-leading language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Generative AI, Large Language Model (LLM), Natural Language Understanding (NLU), Machine Learning (ML), Retrieval-Augmented Generation, Responsible AI, Agent, Evaluation, and Model Adaptation. As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding. The Science team at AWS Bedrock builds science foundations of Bedrock, which is a fully managed service that makes high-performing foundation models available for use through a unified API. We are adamant about continuously learning state-of-the-art NLP/ML/LLM technology and exploring creative ways to delight our customers. In our daily job we are exposed to large scale NLP needs and we apply rigorous research methods to respond to them with efficient and scalable innovative solutions. At AWS Bedrock, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging AWS resources, one of the world’s leading cloud companies and you’ll be able to publish your work in top tier conferences and journals. We are building a brand new team to help develop a new NLP service for AWS. You will have the opportunity to conduct novel research and influence the science roadmap and direction of the team. Come join this greenfield opportunity! Amazon Bedrock team is part of Utility Computing (UC) About the team 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. 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: Seattle, WA, USA