Careers

We are seeking an Applied Scientist to join our Generate AI Visual Media - Core Experiences Science team. This organization drives significant business value by revolutionizing how customers interact with product imagery and videos across Amazon. Working at the intersection of Computer Vision and Generative AI, you will help customers make confident purchase decisions through optimized visual content. Our goal is to transform shopping into a personalized journey of inspiration, discovery, and evaluation by helping customers evaluate how products will fit and flatter their unique self before they ship. In this role, you will be responsible for building scalable computer vision and machine learning (CVML) models, and automating their application and expansion to power customer-facing features. We are seeking domain expertise specifically in 3D Generative AI and Inverse Rendering. Relevant topics include Neural Fields, Implicit 3D Representations, NeRFs, Differentiable Rendering/Simulation, and Physically Based Rendering (PBR) Materials. We expect strong expertise at the intersection of Computer Graphics, Computer Vision, and Deep Learning. An inclination towards Generative AI, such as GANs, VQ-VAE/GAN, and Diffusion Models, is essential. Additionally, experience in application-specific domains such as Neural Avatars, Parametric Body Models, and Differentiable Rendering/Simulation will be highly advantageous. Key job responsibilities - Explore, collect, and process data - Frame and transform ambiguous business challenges into science hypotheses. Design and implement offline and online experiments to evaluate them - Develop prototypes to test new concepts/proposals for models and algorithms - Design and build automated, scalable pipelines to train and deploy ML models

At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. ABOUT THIS ROLE As an Applied Scientist, you will solve large complex real-world problems at scale, draw inspiration from the latest science and technology to empower undefined/untapped business use cases, delve into customer requirements, collaborate with tech and product teams on design, and create production-ready models that span various domains, including Machine Learning (ML), Artificial Intelligence (AI), Natural Language Processing (NLP), Reinforcement Learning (RL), real-time and distributed systems. As an Applied Scientist on our AI Acceleration Team, you will be at the forefront of transforming how Audible harnesses the power of AI to enhance productivity, unlock new value, and reimagine how we work. In this unique role, you'll apply ML/AI approaches to solve complex real-world problems while helping build the blueprint for how Audible works with AI. ABOUT YOU You are passionate about applying scientific approaches to real business challenges, with deep expertise in Machine Learning, Natural Language Processing, GenAI, and large language models. You thrive in collaborative environments where you can both build solutions and empower others to leverage AI effectively. You have a track record of developing production-ready models that balance scientific excellence with practical implementation. You're excited about not just building AI solutions, but also creating frameworks, evaluation methodologies, and knowledge management systems that elevate how entire organizations work with AI. As an Applied Scientist, you will... - Design and implement innovative AI solutions across our three pillars: driving internal productivity, building the blueprint for how Audible works with AI, and unlocking new value through ML & AI-powered product features - Develop machine learning models, frameworks, and evaluation methodologies that help teams streamline workflows, automate repetitive tasks, and leverage collective knowledge - Enable self-service workflow automation by developing tools that allow non-technical teams to implement their own solutions - Collaborate with product, design and engineering teams to rapidly prototype new product ideas that could unlock new audiences and revenue streams - Build evaluation frameworks to measure AI system quality, effectiveness, and business impact - Mentor and educate colleagues on AI best practices, helping raise the AI fluency across the organization ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.

We are looking for a Data Scientist to join our Prime Video team in Israel, focusing on personalizing customer experiences through Search and Recommendations. Our team leverages Machine Learning (ML) to deliver tailored content discovery, helping millions of customers find the entertainment they love. You will work on large-scale experimentation, measurement frameworks, and data-driven decision-making that directly shapes how customers interact with Prime Video. Key job responsibilities - Design metrics frameworks and evaluation systems to measure the quality, performance, and reliability of algorithmic solutions - Lead the design, execution, and analysis of A/B tests to validate product hypotheses and quantify customer impact - Communicate analytical findings and recommendations clearly to both technical teams and business stakeholders, driving data-informed decisions - Partner with Applied Scientists, Software Engineers, and Product Managers to define requirements, evaluate models, and drive data-informed product decisions - Act as the subject matter expert for data structures, metrics definitions, and analytical best practices - Identify opportunities for improving customer experience through deep-dive analyses of user behavior and algorithm performance

Are you a scientist interested in pushing the state of the art in Information Retrieval, Large Language Models and Recommendation Systems? Are you interested in innovating on behalf of millions of customers, helping them accomplish their every day goals? Do you wish you had access to large datasets and tremendous computational resources? Do you want to join a team of capable scientist and engineers, building the future of e-commerce? Answer yes to any of these questions, and you will be a great fit for our team at Amazon. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, generative AI, large-scale data systems, and user experience design to deliver the best shopping experiences for our customers. Our team is building next-generation personalization systems powered by Large Language Models. We are tackling novel research challenges to help customers discover products they'll love - at Amazon scale and latency requirements. We are a team uniquely placed within Amazon, to have a direct window of opportunity to influence how customers will think about their shopping journey in the future. As an Applied Science Manager, you will lead a team of scientists working at the frontier of LLM-based personalization. You will set the technical vision, drive the research agenda, and ensure your team delivers production-ready solutions. You will hire, mentor, and develop world-class scientists while fostering a culture of innovation and scientific rigor. You will partner closely with engineering and product teams to translate ambitious research into customer-facing impact, and represent your team's work to senior leadership. Please visit https://www.amazon.science for more information.

Join us in the evolution of Amazon’s Seller business! The Selling Partner Growth organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential supported by Amazon tools and resources. We are looking for an Applied Scientist II to work on our growth agent vision on seller recommendation to improve our SP growth strategy and drive new seller success. As a successful applied scientist on our talented team of applied scientists and economists, you will translate complex business problems into science solutions using a variety of machine learning techniques, and collaborate with engineering, research, and business teams to deliver impactful experiences on behalf of our sellers. You need to have deep understanding of the business domain and have the ability to bridge business needs with scientific approaches. You are also strong in machine learning methodology and scientific foundation with the ability to collaborate with engineering to put models in production to answer specific business questions. You excel at identifying the right ML techniques—whether supervised learning, causal inference, optimization, or other approaches—to solve diverse business challenges. You are an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication. You will continue to contribute to the research community, by working with scientists across Amazon, as well as collaborating with academic researchers and publishing papers (www.aboutamazon.com/research). Key job responsibilities As an Applied Scientist II in the team, you will: - Identify opportunities to improve SP growth and translate those opportunities into science problems. - Design and execute roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers. - Work with our engineering partners and draw upon your experience to meet latency and other system constraints. - Be responsible for communicating our science innovations to the broader internal & external scientific community.

Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA), you'll have the opportunity to reinvent the hiring process and deliver unprecedented scale, sophistication, and accuracy for Amazon Talent Acquisition operations. ITA is an industry-leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise. Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time. You’ll work on state-of-the-art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in-house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives. Together, we can solve the world's toughest hiring problems. Recruiting Agents and Candidate Voice team is revolutionizing how Amazon finds and connects with talent worldwide! We're looking for an experienced Applied Scientist to design and implement agentic solutions that help millions of candidates find their dream jobs at Amazon. Key job responsibilities • Design and architect AI-powered agentic solutions that help candidates navigate Amazon's hiring process, including scoping requirements, identifying dependencies and constraints, and creating robust scientific and technical designs that balance candidate experience with system scalability. • Implement and deploy conversational AI agents leveraging state-of-the-art LLM and GenAI technologies to enable candidates to explore job opportunities, understand role requirements, and receive personalized guidance throughout their hiring journey. • Develop rigorous evaluation frameworks to measure agent effectiveness, candidate satisfaction, and hiring outcomes—continuously iterating on models to improve accuracy, fairness, and user experience across millions of candidate interactions. • Collaborate cross-functionally with Research Scientists, Software Engineers, and Product teams to integrate agentic solutions into Amazon's candidate-facing platforms, ensuring seamless deployment and alignment with broader Talent Acquisition goals. • Drive innovation in agentic AI research by staying current with advances in NLP, LLMs, and autonomous agent architectures, while contributing to the scientific community through publications, internal tech talks, and knowledge sharing. About the team Our team focuses on understanding and improving the experience of both job seekers and the recruiters who support them. You'll be at the intersection of people, data, and technology—solving fascinating problems that directly impact how we hire the best talent globally.

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: - Evaluate and optimize thermal solution requirements of consumer electronic products - Use simulation tools like Star-CCM+ or FloTherm XT/EFD for analysis and design of products - Validate design modifications for thermal concerns using simulation and actual prototypes - Establish temperature thresholds for user comfort level and component level considering reliability requirements - Have intimate knowledge of various materials and heat spreaders solutions to resolve thermal issues - Use of programming languages like Python and Matlab for analytical/statistical analyses and automation - Collaborate as part of device team to iterate and optimize design parameters of enclosures and structural parts to establish and deliver project performance objectives - Design and execute of tests using statistical tools to validate analytical models, identify risks and assess design margins - Create and present analytical and experimental results - Develop and apply design guidelines based on project learnings 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 devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create?

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising solutions that drive product discovery and sales. We deliver billions of ad impressions every single day on behalf of our advertisers. You'll work with us to help our Advertising teams make sense of the torrent of data produced by the advertising lifecycle. We are using SOTA generative AI to help teams generate insights faster based on our massive data lake. You will need to invent new techniques for metrics retrieval and SQL generation to ensure we're retrieving accurate and trusted data. You'll create feedback loops to ensure our solution is constantly evaluating itself and improving. Being that this is for a conversational AI position, here is what our bot replied when we prompted it for a job description of who should help build it: Role Overview: We are looking for an exceptional applied scientist to join our team building SpektrBot, a conversational AI assistant that helps data engineers and analysts with their workflows. You will work closely with engineers and product managers to design, implement, and optimize natural language processing models like intent classification, named entity recognition, question answering, etc. that enable our Ads chatbot to understand user requests and have natural conversations. Responsibilities: Study and understand data engineering and analytics workflows to design the right conversational experiences Research, design, and develop NLP/NLU models for intent classification, entity extraction, sentiment analysis etc. Continuously improve models through techniques like active learning, transfer learning etc. Optimize models for metrics like precision, recall, latency, interpretability etc. Implement models within overall bot architecture and integrate with backend systems Collaborate with engineers to productionize and monitor models Stay up-to-date on latest advancements in conversational AI research, specifically in LLMs (multi-agent, chain of thought, autonomous agents) Be familiar with optimizing retrievers in RAG architectures. Key job responsibilities You will test multiple foundational models and fine tune when appropriate. You will create feedback loops that will evaluate performance and improve our systems. You will optimize prompts for better responses from our LLMs. You will build tools to auto-curate metadata using LLMs. A day in the life You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. About the team We have a small scrappy team carved out from a large Ads wide data lake team. We are swimming in petabytes of data that we help the organization make sense of. Our team's mission is to help anyone in the Ads org find the data they need using only natural language. We are a supportive and collaborative team who iterates quickly and shares in each others' successes.

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. Key job responsibilities - Work backwards from customer problems to research and develop novel machine learning solutions for music and podcast recommendations. Through A/B testing and online experiments done hand-in-hand with engineering teams, you'll implement and validate your ideas and solutions. - Advocate solutions and communicate results, insights and recommendations to stakeholders and partners. - Produce innovative research on recommender systems that shapes the field and meets the high standards of peer-reviewed publications. You'll cement your team's reputation as thought leaders pioneering new recommenders. Stay current with advancements in the field, adapting latest in literature to build efficient and scalable models A day in the life Lead innovation in AI/ML to shape Amazon Music experiences for millions. Develop state of the art models leveraging and advancing the latest developments in machine learning and genAI. Collaborate with talented engineers and scientists to guide research and build scalable models across our audio portfolio - music, podcasts, live streaming, and more. Drive experiments and rapid prototyping, leveraging Amazon's data at scale. Innovate daily alongside world-class teams to delight customers worldwide through personalization. About the team The team is responsible for models that underly Amazon Music’s recommendations content types (music, podcasts, audiobooks), sequencing models for algorithmic stations across mobile, web and Alexa, ranking models for the carousels and Page strategy on Amazon Music surfaces, and Query Understanding for conversational flow and recommendations. You will collaborate with a team of product managers, applied scientists and software engineers delivering meaningful recommendations, personalized for each of the millions of customers using Amazon Music globally. As a scientist on the team, you will be involved in every aspect of the development lifecycle, from idea generation and scientific research to development and deployment of advanced models. You will work closely with engineering to realize your scientific vision.

Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as an Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Please visit https://www.amazon.science for more information Key job responsibilities - Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the international seller services domain. - Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. - Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. - Continuously explore and evaluate state-of-the-art NLP techniques and methodologies to improve the accuracy and efficiency of language-related systems. - Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. - Mentor and guide team of Applied Scientists from technical and project advancement stand point - Contribute research to science community and conference quality level papers.