Careers

Every day, hundreds of millions of customers arrive on Amazon's Homepage, search for a product, and land on a detail page. Along the way, some of what we show them is irrelevant — a recommendation that misreads what they want, a widget that repeats what they just saw, a headline that doesn't say what it means, a product that shouldn't have been surfaced at all. We have built the ability to detect these defects at scale using Large Language Models (LLMs), and we report them to Amazon's most senior leadership as a company-level measure of shopping quality. What we have not yet built is the confidence to act on every one of them. That is the problem you will own. Today, the most consequential categories of shopping defects — product quality, duplication, staleness, irrelevance — go largely unenforced. Not because we cannot detect them, but because we have not yet clearly defined when they lead to customer dissatisfaction. These are genuinely hard questions. When is a recommendation irrelevant rather than merely unexpected? When are two products duplicates rather than legitimate alternatives? Every answer carries consequences for customers and advertisers. As Principal Data Scientist, you will own the analytical rigor behind Amazon's store quality metrics end-to-end: how they are defined, how faithfully our LLM implementations execute those definitions, and how accurate the resulting judgments actually are across relevance, presentation, product quality, and duplication. You will go deep enough to inspect individual model judgments and read the edge cases where they break, then come back up to argue definitional questions with the leaders who own the outcomes on both sides. You will also define experiments that will turn judgement into facts backed by data. Your influence will come from deep dives so well-constructed that stakeholders across two large organizations change their minds. Getting there means building the tooling that makes rigor repeatable rather than heroic: agents that walk the store the way a customer would, automated inspection of experiments, and feedback loops that finally connect what customers tell us directly to the metrics we optimize. Much of the customer voice we already collect goes underused today; you will change that. You will begin on the Homepage, where measurement is most mature, then extend the methodology to Detail Page and Search — carrying not just the metrics but the standard of evidence with them. Key job responsibilities - Validate store quality metrics end-to-end: inspect metric definitions, audit LLM implementations, and evaluate LLM judgment accuracy across quality dimensions (relevance, presentation, product quality, duplicates) - Starting with Homepage, extending cross-page — identify gaps and inconsistencies between Organic and Ads treatments that block the tiered enforcement framework - Drive alignment through deep dives that present evidence to both Stores and Ads stakeholders - Build and deliver deep-dive tooling (walk-the-store bots, automated experiment inspection via Gecko, VoC feedback loops) that make metric validation repeatable and self-serve - Own the analytical rigor behind the alignment on definitions of subjective/debated metrics and moving them to aligned/enforceable About the team Core Shopping Data Science owns the measurement of shopping quality across Amazon's Core Shopping eexperiences, including defect metrics reviewed by Amazon's most senior leadership. We focus on the long term and big picture to ensure that the full Amazon shopping experience balances strategic trade-offs. We work across Stores and Advertising, partnering with personalization, ranking, and search teams to turn measurement into changes customers can feel.

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs), ASR, TTS, and Speech to Speech models, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services. Key job responsibilities As an Applied Scientist with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with Large Language Models (LLMs), ASR, TTS, and Speech to Speech model. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Drive research in ASR, TTS, and Speech-to-Speech (S2S) model training and fine-tuning * Advance multilingual speech recognition and synthesis using LLM-based architectures * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Collaborate with partner teams on evaluation frameworks and post-training methodologies. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.

Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities Strategic Leadership & Vision - Define long-term science vision for Traffic Quality driven by advertiser and publisher needs, translating direction into actionable team plans. - Solve strategically important business problems independently, delivering robust, scalable scientific solutions with limited guidance. - Proactively identify technology gaps and business opportunities, determining resource allocation priorities. Scientific Innovation & Execution - Design and implement statistical and machine learning solutions to detect robotic and human traffic patterns across billions of daily ad events. - Own full development cycle for production-level code handling billions of ad requests: design, prototype, A/B testing, and deployment. - Stay current with scientific advancements and build publication strategy while championing excellence best practices. - Directly protect hundreds of millions of dollars in advertiser spend annually while maintaining seamless user experience. - Partner with engineers, product managers, and cross-functional teams to solve complex IVT detection problems and influence strategic initiatives. - Mentor scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. We are looking for an Applied Scientist to push the envelope of AI content generation. As a scientist at Prime Video, you will contribute directly to productions using innovative tools in computer vision, deep learning, and generative AI to transform entertainment experiences. The ideal candidate has deep knowledge in one of: graphics, deep learning, generative AI and/or reinforcement learning and experience applying them real-world problems. You understand tradeoffs between business needs and model complexity, and you take calculated risks in developing rapid prototypes and iterative model improvements. You are excited to learn from and alongside seasoned scientists, engineers, and business leaders. You are an excellent communicator and effectively translate technical findings into production systems and business action (and customer delight). Key job responsibilities • Build generative AI models that create production-ready content, including movie content, localized assets, and visual marketing materials used across Prime Video's global platform. • Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity. • Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models. • Run experiments, gather data, and perform statistical analysis. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research new and innovative machine learning approaches. • Share knowledge and research outcomes via internal and external conferences and journal publications A day in the life In this role, you will invent science and systems for content localization, generation, including graphics and machine learning-based modeling systems. You will work with a team of scientists and product managers to design customer-facing products, and you will work with technology teams to productize and maintain the associated solutions.

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 Search Ranking and Interleaving (R&I) team within Sponsored Products and Brands is responsible for determining which ads to show and the quality of ads shown on the search page (e.g., relevance, personalized and contextualized ranking to improve shopper experience, where to place them, and how many ads to show on the search page. This helps shoppers discover new products while helping advertisers put their products in front of the right customers, aligning shoppers’, advertisers’, and Amazon’s interests. To do this, we apply a broad range of GenAI and ML techniques to continuously explore, learn, and optimize the ranking and allocation of ads on the search page. We are an interdisciplinary team with a focus on improving the SP experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. A day in the life As a Sr. Applied Scientist on this team, you will identify big opportunities for the team to make a direct impact on customers and the search experience. You will work closely with with search and retail partner teams, software engineers and product managers to build scalable real-time GenAI and ML solutions. You will lead projects end-to-end including problem formulation in collaboration with Product Managers and key stakeholders, lead a team of scientists to build models, design, run, and analyze A/B experiments to improve the experience of millions of Amazon shoppers. Key job responsibilities - Architect next-generation systems that improve Amazon's Search page. - Pioneer breakthrough applied science in multi-modal GenAI applications for advertising, combining text, image, and multi-lingual support. - Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. - Research new and innovative machine learning approaches.

Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.

Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and exclusive access to coverage of live sports. All customers regardless of whether they have a Prime membership or not, can access programming from subscriptions such as Apple TV, Peacock Premium Plus, HBO Max, FOX One, Crunchyroll and MGM+, as well as more than 900 free ad-support (FAST) Channels, rent or buy titles, and enjoy even more content for free with ads. The Prime Video Personalization and Discovery team matches customers with the right content at the right time, at all touch points throughout the content discovery journey. We are looking for a customer-focused, solutions-oriented Data Scientist to help build new data-driven frameworks to understand what makes new personalization and content discovery innovations successful for users and the business. You'll be part of an embedded science team on projects that are fast-paced, challenging, and ultimately influence what millions of customers around the world see when the log into Prime Video. The ideal candidate brings strong problem-solving skills, stakeholder communication skills, and the ability to balance technical rigor with delivery speed and customer impact. You will build cross-functional support within Prime Video, assess business problems, define metrics, and support iterative scientific solutions that balance short-term delivery with long-term science roadmaps. Key job responsibilities - Use advanced statistical and machine learning techniques to extract insights from complex, large-scale data sets - Design and implement end-to-end data science workflows, from data acquisition and cleaning to model development, testing, and deployment - Support scalable, self-service data analyses by building datasets for analytics, reporting and ML use cases - Partner with product stakeholders and senior science peers to identify strategic data-driven opportunities to improve the customer experience - Communicate findings, conclusions, and recommendations to technical and non-technical stakeholders - Stay up-to-date on the latest data science tools, techniques, and best practices and help evangelize them across the organization

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music. The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Other responsibilities include: - Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks. - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Research and implement novel machine learning and statistical approaches - Implement scalable data pipelines and model-serving systems. - Analyze experimental results, draw insights, and refine models to improve accuracy and robustness. - Communicate findings and recommendations to technical and non-technical audiences.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music. The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Other responsibilities include: - Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks. - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Research and implement novel machine learning and statistical approaches - Implement scalable data pipelines and model-serving systems. - Analyze experimental results, draw insights, and refine models to improve accuracy and robustness. - Communicate findings and recommendations to technical and non-technical audiences.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music. The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Other responsibilities include: - Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks. - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Research and implement novel machine learning and statistical approaches - Implement scalable data pipelines and model-serving systems. - Analyze experimental results, draw insights, and refine models to improve accuracy and robustness. - Communicate findings and recommendations to technical and non-technical audiences.