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

US, NY, New York
The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist III Job Location: Seattle, Washington Job Number: AMZ9674365 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. Position Requirements: Master's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science or a related field and two years of experience in the job offered or a related occupation. Employer will accept a Bachelor's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or a related field and five years of progressive post-baccalaureate experience in the job offered or a related occupation as equivalent to the Master's degree and two years of experience. Must have one year of experience in the following skills: (1) building statistical models and machine learning models using large datasets from multiple resources; (2) building complex data analyses by leveraging scripting languages including Python, Java, or related scripting language; and (3) communicating with users, technical teams, and management to collect requirements, evaluate alternatives, and develop processes and tools to support the organization. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $162,752/year to $215,300/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits.#0000
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. 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. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.
US, CA, Sunnyvale
Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like Echo, Fire Tablets, Fire TV, and other consumer devices. We are looking for exceptional scientists to join our Applied Science team to help build industry-leading technology with multimodal language models for various edge applications. This role is for a Sr. Applied Scientist to lead science efforts for on-device inference pipelines and orchestration, working closely with cross-functional product and engineering teams to invent, design, develop, and validate new AI features for our devices. Key job responsibilities * Lead cross-functional efforts to invent, design, develop, and validate new AI features for our devices * Invent, build, and evaluate model inference and orchestrations to enable new customer experiences * Drive partnerships with product and engineering teams to implement algorithms and models in production * Train and optimize state-of-the-art multimodal models for resource-efficient deployment * Work closely with compiler engineers, hardware architects, data collection, and product teams A day in the life As an Applied Scientist with the Silicon and Solutions Group Edge AI team, you'll contribute to science solution design, conduct experiments, explore new algorithms, develop embedded inference pipelines, and discover ways to enrich our customer experiences. You'll have opportunities to collaborate across teams of engineers and scientists to bring algorithms and models to production. About the team Our Devices team specializes in inventing new-to-world, category creating products using advanced machine learning technologies. This role is on a new cross-functional team, whose cadence and structure resembles an efficient and fast-paced startup, with rapid growth and development opportunities.
US, WA, Seattle
About Sponsored Products and Brands: The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About Our Team: The Sponsored Brands Impressions-based Offerings team is responsible for evolving the value proposition of Sponsored Brands to drive brand advertising in retail media at scale, helping brands get discovered, acquire new customers and sustainably grow customer lifetime value. We build end-to-end solutions that enable brands to drive discovery, visibility and share of voice. This includes building advertiser controls, shopper experiences, monetization strategies and optimization features. We succeed when (1) shoppers discover, engage and build affinity with brands and (2) brands can grow their business at scale with our advertising products. About This Role: As an Applied Scientist on our team, you will: * Develop AI solutions for Sponsored Brands advertiser and shopper experiences. Build monetization and optimization systems that leverage generative models to value and improve campaign performance. * Define a long-term science vision and roadmap for our Sponsored Brands advertising business, driven from our customers' needs, translating that direction into specific plans for applied scientists and engineering teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. * Design and conduct A/B experiments to evaluate proposed solutions based on in-depth data analyses. * Effectively communicate technical and non-technical ideas with teammates and stakeholders; * Stay up-to-date with advancements and the latest modeling techniques in the field. * Think big about the arc of development of Gen AI over a multi-year horizon and identify new opportunities to apply these technologies to solve real-world problems. #GenAI
US, MA, N.reading
Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Collaborate with simulation and robotics experts to translate physical modeling needs into robust, scalable, and maintainable simulation solutions. - Design and implement high-performance simulation modeling and tools for rigid and deformable body simulation. - Identify and optimize performance bottlenecks in simulation pipelines to support real-time and batch simulation workflows. - Help build validation and unit testing pipelines to ensure correctness and physical fidelity of simulation results. - Identify potential sources of sim-to-real gaps and propose modeling and numerical approximations to reduce them. - Stay current with the latest advances in numerical methods, parallel computing, and GPU architectures, and incorporate them into our tools.
IN, KA, Bengaluru
Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health Wellness, Amazon Echo & Astro products. This is an exciting opportunity to join Amazon in developing state-of-the-art techniques that bring Gen AI on edge for our consumer products. We are looking for exceptional scientists to join our Applied Science team and help develop the next generation of edge models, and optimize them while doing co-designed with custom ML HW based on a revolutionary architecture. Work hard. Have Fun. Make History. Key job responsibilities Quantize, prune, distill, finetune Gen AI models to optimize for edge platforms Fundamentally understand Amazon’s underlying Neural Edge Engine to invent optimization techniques Analyze deep learning workloads and provide guidance to map them to Amazon’s Neural Edge Engine Use first principles of Information Theory, Scientific Computing, Deep Learning Theory, Non Equilibrium Thermodynamics Train custom Gen AI models that beat SOTA and paves path for developing production models Collaborate closely with compiler engineers, fellow Applied Scientists, Hardware Architects and product teams to build the best ML-centric solutions for our devices Publish in open source and present on Amazon's behalf at key ML conferences - NeurIPS, ICLR, MLSys.
IN, KA, Bengaluru
You will be working with a unique and gifted team developing exciting products for consumers. The team is a multidisciplinary group of engineers and scientists engaged in a fast paced mission to deliver new products. The team faces a challenging task of balancing cost, schedule, and performance requirements. You should be comfortable collaborating in a fast-paced and often uncertain environment, and contributing to innovative solutions, while demonstrating leadership, technical competence, and meticulousness. Your deliverables will include development of thermal solutions, concept design, feature development, product architecture and system validation through to manufacturing release. You will support creative developments through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques. Key job responsibilities In this role, you will: - Lead end-to-end thermal design for SoC and consumer electronics, spanning package, board, system architecture, and product integration - Perform advanced CFD simulations using tools such as Star-CCM+ or FloEFD to assess feasibility, risks, and mitigation strategies - Plan and execute thermal validation for devices and SoC packages, ensuring compliance with safety, reliability, and qualification requirements - Partner with cross-functional and cross-site teams to influence product decisions, define thermal limits, and establish temperature thresholds - Develop data processing, statistical analysis, and test automation frameworks to improve insight quality, scalability, and engineering efficiency - Communicate thermal risks, trade-offs, and mitigation strategies clearly to engineering leadership to support schedule, performance, and product decisions About the team Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced innovative devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create?
CA, BC, Vancouver
Success in any organization begins with its people and having a comprehensive understanding of our workforce and how we best utilize their unique skills and experience is paramount to our future success. WISE (Workforce Intelligence powered by Scientific Engineering) delivers the scientific and engineering foundation that powers Amazon's enterprise-wide workforce planning ecosystem. Addressing the critical need for precise workforce planning, WISE enables a closed-loop mechanism essential for ensuring Amazon has the right workforce composition, organizational structure, and geographical footprint to support long-term business needs with a sustainable cost structure. We are looking for a Sr. Applied Scientist to join our ML/AI team to work on Advanced Optimization and LLM solutions. You will partner with Software Engineers, Machine Learning Engineers, Data Engineers and other Scientists, TPMs, Product Managers and Senior Management to help create world-class solutions. We're looking for people who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history. You will leverage your knowledge in machine learning, advanced analytics, metrics, reporting, and analytic tooling/languages to analyze and translate the data into meaningful insights. You will have end-to-end ownership of operational and technical aspects of the insights you are building for the business, and will play an integral role in strategic decision-making. Further, you will build solutions leveraging advanced analytics that enable stakeholders to manage the business and make effective decisions, partner with internal teams to identify process and system improvement opportunities. As a tech expert, you will be an advocate for compelling user experiences and will demonstrate the value of automation and data-driven planning tools in the People Experience and Technology space. Key job responsibilities * Engineering execution - drive crisp and timely execution of milestones, consider and advise on key design and technology trade-offs with engineering teams * Priority management - manage diverse requests and dependencies from teams * Process improvements – define, implement and continuously improve delivery and operational efficiency * Stakeholder management – interface with and influence your stakeholders, balancing business needs vs. technical constraints and driving clarity in ambiguous situations * Operational Excellence – monitor metrics and program health, anticipate and clear blockers, manage escalations To be successful on this journey, you love having high standards for yourself and everyone you work with, and always look for opportunities to make our services better.
RO, Bucharest
Amazon's Compliance and Safety Services (CoSS) Team is looking for a smart and creative Applied Scientist to apply and extend state-of-the-art research in NLP, multi-modal modeling, domain adaptation, continuous learning and large language model to join the Applied Science team. At Amazon, we are working to be the most customer-centric company on earth. Millions of customers trust us to ensure a safe shopping experience. This is an exciting and challenging position to drive research that will shape new ML solutions for product compliance and safety around the globe in order to achieve best-in-class, company-wide standards around product assurance. You will research on large amounts of tabular, textual, and product image data from product detail pages, selling partner details and customer feedback, evaluate state-of-the-art algorithms and frameworks, and develop new algorithms to improve safety and compliance mechanisms. You will partner with engineers, technical program managers and product managers to design new ML solutions implemented across the entire Amazon product catalog. Key job responsibilities As an Applied Scientist on our team, you will: - Research and Evaluate state-of-the-art algorithms in NLP, multi-modal modeling, domain adaptation, continuous learning and large language model. - Design new algorithms that improve on the state-of-the-art to drive business impact, such as synthetic data generation, active learning, grounding LLMs for business use cases - Design and plan collection of new labels and audit mechanisms to develop better approaches that will further improve product assurance and customer trust. - Analyze and convey results to stakeholders and contribute to the research and product roadmap. - Collaborate with other scientists, engineers, product managers, and business teams to creatively solve problems, measure and estimate risks, and constructively critique peer research - Consult with engineering teams to design data and modeling pipelines which successfully interface with new and existing software - Publish research publications at internal and external venues. About the team The science team delivers custom state-of-the-art algorithms for image and document understanding. The team specializes in developing machine learning solutions to advance compliance capabilities. Their research contributions span multiple domains including multi-modal modeling, unstructured data matching, text extraction from visual documents, and anomaly detection, with findings regularly published in academic venues.