Careers

The AWS Neuron Science Core Algorithm team is looking for talented Applied Scientists to push the frontier of hardware-aware machine learning for Trainium and Inferentia, the AWS Machine Learning accelerators. In this rare role at the intersection of LLM modeling, large-scale training systems, and hardware/datatype co-design, you own model and algorithm decisions jointly with AWS custom silicon. You will own solutions end-to-end from research through production, publish at top venues, and work alongside distinguished engineers and scientists in a strategic growth area for AWS. We actively work on these areas: - Low-precision training and inference: MXFP8, MXFP4, and sub-4-bit training and inference recipes, stochastic rounding, and Trn4 datatype exploration. - Trn-friendly architectures: model architectures that exploit hardware strengths without sacrificing quality. - System-aware optimizers & efficient distributed systems: efficient optimizers and distributed system that gives best accuracy, co-designed with the hardware. - Foundation-model pre-training accuracy: end-to-end validation across model scales, catching training divergence early, and equivalence-checking tooling. - GenAI for systems: RL post-training for NKI kernel generation, mitigating reward-hacking and accelerating under low precision on Trn. Key job responsibilities - Own scientific problems end-to-end - from research and experimentation through production impact - applying rigorous evaluation to complex, ill-defined problems at large scale. - Develop production-quality code in PyTorch or JAX and integrate scientific components into large-scale training and inference systems with operational excellence and efficient resource usage. - Partner with foundation-model, engineering, and hardware-architecture teams so your findings directly inform what gets built into Trainium and shipped in the product stack. - Mentor fellow scientists and interns, give constructive peer reviews, and help shape team goals, priorities, and the technical roadmap. - Author and publish research at top peer-reviewed venues (ICLR, NeurIPS, ICML, MLSys) and engage the broader scientific community. A day in the life You might start your morning reviewing large-scale training runs — checking accuracy at a new low-precision datatype or debugging a divergence before it costs a run — then join a design discussion with engineering partners on how to land your recipe in the production stack. After lunch you could be whiteboarding a Trn-friendly architecture variant or an RL post-training approach for kernel generation with a teammate, then writing code to prototype it on Trainium. You will regularly present findings to the team and to leadership, review peers' and interns' work, and stay connected with the academic community. About the team AWS Neuron is the software of Trainium and Inferentia, the AWS Machine Learning chips. Inferentia delivers best-in-class ML inference performance at the lowest cost in the cloud to our AWS customers. Trainium is designed to deliver the best-in-class ML training performance at the lowest training cost in the cloud, and it's all being enabled by AWS Neuron. Neuron is a software that includes an ML compiler and native integration into popular ML frameworks. Our products are being used at scale with external customers like Anthropic and Databricks as well as internal customers like Amazon FMR, Amazon AGI, Amazon Bedrock, Amazon Robotics, Amazon Ads, and many more.

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 technologist, you'll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. 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. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader, improving how customers search for and discover what to watch. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Prime Video customers worldwide. Key job responsibilities Develop machine learning solutions for Prime Video Search systems, including query understanding, retrieval, and ranking, using deep learning, reinforcement learning, and optimization methods; Work closely with engineers and product managers to design, implement and launch machine learning solutions end-to-end; Design and conduct offline and online (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; Publish your research findings in top conferences and journals.

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 technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. 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. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Science Manager to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will lead a strong science team and work closely with other science and engineering leaders, product and business partners together to build the best personalized customer experience for Prime Video. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Lead to develop AI solutions for various Prime Video recommendation and personalization systems using Deep learning, GenAI, Reinforcement Learning, recommendation system and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - 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; - Hire and grow a science team working in this exciting video personalization domain. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various devices. We work closely with the engineering teams to launch our solutions in production.

The Foundational AI Assets (FAI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs). Key job responsibilities - Design and implement novel machine learning and deep learning models to address scientifically complex problems, taking solutions from research through production deployment while maintaining high standards for code quality and system scalability. - Drive the scientific agenda for your team by identifying new opportunities, proposing research initiatives, and securing alignment with technical leadership on strategic priorities. - Evaluate and improve model performance using rigorous experimental methodologies, applying techniques such as feature engineering, hyperparameter optimization, and advanced neural network architectures (e.g., Transformers, CNNs) built with frameworks like PyTorch or TensorFlow. - Mentor and coach scientists and engineers across the organization, conduct thorough code and design reviews, and contribute to the professional growth of your peers while raising the technical bar for the team. - Collaborate cross-functionally with engineering, product, and science partners to translate ambiguous business problems into well-defined scientific approaches, balancing short-term customer needs with long-term technical vision. A day in the life You might start your morning reviewing experiment results and refining a model architecture before joining a design review where you provide guidance on a colleague's approach to a new problem. After lunch, you dive into writing production-quality code for a critical component of your system, then shift to drafting a scientific document that outlines your team's next research direction. Throughout, you engage with engineers and scientists across teams, sharing your expertise and learning from theirs. About the team The FAI team has the mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.

The Foundational AI Assets (FAI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs). Key job responsibilities As an Applied Scientist with the FAI team, you will support the development of RL Gyms, assess their usefulness for the frontier model advancement and build techniques to advance the state of the art with LLMs. You will support the foundational model development in an applied research role. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. About the team The FAI team has the mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.

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 We are seeking a data scientist builder to join the Audible economics team. Our group of economists, data scientists, and analysts tackles a wide range of questions, including pricing, experimentation science, data-driven product strategy/optimizations, internal productivity/incentives, audience science, and impact/ROI measurement. The ideal candidate will enjoy wearing many hats, possess an economist's mindset and a strong ability to effectively translate business questions into tractable quantitative frameworks, and excel at leveraging AI to build and scale robust, interpretable, and production-ready models/systems/tools. We're looking for someone who automates the repetitive, builds tools that force-multiply the team's output/influence, and treats AI as a core part of their workflow - not a side project. If you are passionate about leveraging data to shape the future of digital media, we encourage you to apply and be a part of our dynamic team. As a Data Scientist, you will... - Collaborate with economists, analysts, and other data scientists to build and scale econometric/ML models and quantitative tools - owning end-to-end scoping, data pipelining, feature engineering, model development/refinement, production-grade deployment, impact measurement, and adoption - Research and evaluate emerging tools and techniques (AI-driven and otherwise), and identify novel data sources to leverage in quantitative work – both from within Audible/Amazon and from 3P sources - Collaborate closely with Product, Content, and Marketing partners to drive broad impact and ensure that solutions are integrated into cross-functional workflows and executive decision-making - Represent the team in a range of settings - from reviews with senior Amazon scientists to reviews with senior Audible/Amazon business leaders - Mentor junior scientists and raise the bar for a new generation of scalable, AI-enabled science/analytical work, both within Audible and across the broader Amazon community 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. Key job responsibilities

Are you passionate about giving customers the richest, most inspiring experience in their shopping journey? Do you like to dive deep to understand how customer-centric solutions drive measurable results? Do you enjoy working closely with the business and software engineers to design rigorous experiments, build the data infrastructure behind them, and translate results into decisions? You are in the right place! Come join our Prime & Marketing Analytics and Science (PRIMAS) team, where your work will directly impact millions of customers. The EU Marketing & Prime organization is looking for a Data Scientist to join the PRIMAS team. This role sits at the intersection of applied statistics and large-scale analytics — you'll design experiments and causal models, and also own the data pipelines, metrics, and reporting infrastructure that make those results usable across the business. The PRIMAS team provides a comprehensive understanding of customer segments, affinities, and lifetime value. We use data science tools and advanced statistical techniques to study customer purchase and engagement behaviors, and generate actionable insights on where, when, and how we deliver products and programs to customers. We help increase customer engagement, sales, and marketing efficiency, and our systems are built entirely in-house on automated large-scale analytics infrastructure. You will design, launch, and measure experiments across marketing channels (SEM/SEO, Affiliates, Display, Social, Mobile, Email, Onsite, etc.), engagement products, and customer segments. You will improve our understanding of customer behavior, run rigorous power and minimum detectable effect (MDE) analyses to size experiments correctly, and build the causal and conversion models that value and target our marketing — then build the pipelines and dashboards that keep those signals flowing reliably to stakeholders and downstream systems. You will work at the forefront of consumer analytics, tackling some of the hardest measurement problems in the industry alongside strong scientists, statisticians, and software engineers. Key job responsibilities 1. Design and implement scalable, statistically rigorous experiments (A/B, geo, holdout, quasi-experiments) to measure marketing incrementality across channels. 2. Perform power analysis and minimum detectable effect (MDE) calculations to determine experiment sample sizes, durations, and design trade-offs before launch. 3. Build causal and treatment-effect models that produce conversion and valuation signals consumed by downstream bidding and budgeting systems. 4. Building the ETL, metric definitions, and datasets that make results scalable, extensible, and repeatable rather than one-off analyses. 5. Develop measurement frameworks that quantify the true, platform-independent contribution of marketing over time, and build the dashboards and reporting that keep those metrics visible to the business. 6. Apply statistical, mathematical, and machine learning techniques to solve ambiguous business problems where the right approach isn't obvious. 7. Analyze experiment results for validity — inspecting distributions, checking for sample ratio mismatch, exploring covariate balance, and tracking down the source of anomalies. 8. Communicate experiment design, results, and trade-offs clearly to business and leadership audiences, including inputs into business reviews, and influence decisions and technical direction across teams. 9. Establish scalable, repeatable processes and best practices for experiment design, data modeling, and analysis.

Amazon Advertising Trust is on a mission to redefine how trust is enforced at internet scale, and we are looking for exceptional scientists to help lead the way. We are building systems that reason about advertising content the way a trained human would, at a industry leading volume and speed, and that keep working when the rules change underneath them. At Ads Trust Science we leverage machine learning, generative AI, and large-scale retrieval to solve some of the most complex decision problems for ads worldwide. Every ad shown across Amazon's owned-and-operated properties and third-party networks, in every format and every global marketplace, depends on decisions our systems make. We are just getting started. As a Senior Applied Scientist you will be at the forefront of this work. You will own science problems end to end, bridging the gap between research and production impact. You will not be handed a specification. You will be given a customer problem, a system that partly solves it, and the latitude to decide what to build next. This is a unique opportunity to work at the intersection of multimodal understanding, large language models, and large-scale retrieval, on a problem where the science is genuinely unsettled. You will work alongside scientists whose models run in front of hundreds of millions of customers, at a scale that changes which approaches are even possible. A day in the life - Ship a model into production, then find and document where it fails once real traffic reaches it. - Drive the technical discussion, inside your team and with partner teams, on how to close that gap. - Bring a fresh angle to a problem that is not yours and help a peer get further than they would have alone, building trust across the team in the process. - Mentor while staying hands-on. At this level the design and the code are both yours. About the team Ads Trust Science is a group of scientists who would rather change what the team measures than optimise the wrong number. We value curiosity, rigour, and a bias for action. We expect people to disagree with evidence, to say clearly what did not work, and to iterate quickly toward the solutions that matter.

Do you enjoy solving challenging problems and driving innovation in research? Do you want to develop scalable models and apply machine learning techniques to guide real-world decisions? We are looking for builders, innovators, and entrepreneurs who want to bring their ideas to reality and improve the lives of millions of customers. As a Research Science Intern, you will apply advanced statistical techniques and emerging AI/ML technologies to solve complex problems, implement prototypes, and work with massive datasets. You'll find yourself at the forefront of innovation, shaping the future of Amazon's products, services, and operations. Imagine waking up each morning, fueled by the excitement of solving intricate problems that have a direct impact on Amazon's excellence. Your day might begin by collaborating with cross-functional teams, exchanging ideas and insights to develop innovative solutions. You'll immerse yourself in a world of data, leveraging your expertise in areas such as optimization, machine learning, statistical modeling, and algorithmic research to uncover hidden patterns and drive meaningful impact. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research, and mentorship from industry leaders. This immersive experience will sharpen your technical skills and cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Amazon has positions available for Research Science Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA, Arlington, VA Key job responsibilities • Conduct research activities including data collection, analysis, and interpretation under the guidance of senior researchers • Develop and test hypotheses using appropriate scientific methodologies and computational tools • Document findings, maintain detailed research records, and prepare reports summarizing results and insights • Collaborate with team members to troubleshoot challenges and refine experimental approaches • Participate in team meetings and present progress updates on assigned research projects A day in the life As a Research Science Intern, you'll immerse yourself in hands-on scientific work, collaborating with our research team on projects that span data analysis, experimental design, and computational modeling. Your day might include conducting literature reviews, running simulations, analyzing datasets, and participating in team discussions where your insights contribute to ongoing research initiatives. You'll have opportunities to present findings, learn from mentors, and develop practical skills in a supportive research environment that values curiosity and collaborative problem-solving.