Careers

We are seeking a Research Scientist II to join our Electric Propulsion Subsystem team. In this role, you will be a significant and autonomous contributor, solving difficult engineering problems related to battery management systems (BMS) and energy storage. You will design, develop, and validate BMS hardware and algorithms from concept through production, working on high-voltage lithium-ion battery packs that push the boundaries of performance, safety, and energy density. This position requires an engineer who demonstrates strong technical depth in battery systems and BMS design while also exhibiting the system-level thinking and influence characteristic of our most impactful contributors. Key job responsibilities Design, develop, and validate BMS architectures for high-voltage lithium-ion battery packs from concept through volume production, including cell monitoring and sensing strategy, communication topology, and isolation design Develop safety controls, fault detection and diagnostic mechanisms, cell balancing routines, and thermal protection boundaries, and lead test plans validating BMS designs against safety standards (e.g., UL 2271, IEC 62619, UN 38.3) for functional safety, thermal resilience, and abuse tolerance Develop software algorithms for State of Charge (SOC), State of Health (SOH), and State of Power (SOP) estimation using machine learning models Support Hardware-in-the-Loop (HIL) testing, bench verification, and real-world data analysis to validate BMS algorithms and correlate models with physical pack performance Design charging infrastructure interfaces, including charge profile management, communication protocols, and interoperability with external charging systems Apply system-level thinking to understand how the BMS integrates with and influences the broader product architecture, including power electronics, thermal management, and vehicle control systems, and maintain high-quality design documentation (schematics, algorithm specs, analysis reports, test results)

Description 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 systems that give the 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.

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.

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.

Amazon Advertising drives billions of ad impressions and millions of clicks daily, powering discovery and sales for advertisers across Amazon's Retail and Marketplace businesses. The Ads Marketing Decision Science team sits at the intersection of data science and marketing strategy. We build intelligent, data-driven systems that analyze advertiser behavior at large scale to deliver the right guidance to the right advertiser at the right time. Our work spans behavioral modeling, content intelligence, automated decision systems, and GenAI applications, enabling personalized marketing experiences that help advertisers make smarter advertising decisions and grow their business on Amazon. We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models, design experiments, develop segmentation frameworks, and leverage GenAI capabilities where applicable, taking solutions end-to-end from proof-of-concept to production at scale. You will partner closely with scientists, engineers, and product managers on a daily basis to prototype rapidly, ensure data integrity in production systems, and deliver measurable advertiser impact. If you are passionate about solving real-world problems with cutting-edge science, come join us as we innovate and make history. Key job responsibilities • Define and execute data science solutions end-to-end, from problem framing through production deployment. • Build machine learning models (classification, regression, clustering, ranking) for advertiser segmentation, propensity modeling, and recommendations. • Apply causal inference and experimentation methods (A/B testing, difference-in-differences, propensity score matching) to measure the impact of marketing interventions. • Analyze large-scale advertiser behavioral data to identify trends, surface growth opportunities, and support optimal decision making. • Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale. • Establish and drive data hygiene best practices to ensure coherence and integrity of data feeding into production ML/AI solutions. • Leverage GenAI and LLM capabilities to enhance science products where applicable A day in the life You will solve real-world problems by analyzing large volumes of advertiser data, building predictive models, designing experiments, and measuring business impact. You will prototype rapidly, validate ideas with data, and partner with engineers to productize and scale successful solutions. You will collaborate daily with scientists, engineers, and product managers across the advertising organization, working in a cross-functional, fast-paced environment where data drives decisions and helps advertisers grow. About the team We are a team of Applied Scientists, Research Scientists, Data Scientists, and Business Intelligence Engineers with deep expertise in ML, NLP, Gen-AI, RL, and causal inference, from a diverse range of backgrounds. We partner closely with strong engineers, product managers, and sales leaders who bring ads-industry depth and experience building scalable modeling and software solutions.

Amazon Redshift is the world’s most popular fully managed cloud data warehouse. Tens of thousands of enterprise customers use Redshift to crunch through exabytes of data in the cloud to make business critical decisions every day. To stay ahead in such a mission critical setting, at Redshift, we must always re-invent ourselves for customers. We are always looking for the innovative engineers to help shape the future of Redshift. We are looking for an Applied Scientist to build deep learning models that predict query resource consumption, enabling intelligent workload management at massive scale. Query resource prediction is at the heart of Redshift's workload management, determining how queries are scheduled, scaled, and executed across the system. This is a unique opportunity to shape the future of intelligent query management for the world's most popular cloud data warehouse, powering analytical workloads for Fortune 500 companies, startups, and everything in between. You will bring deep expertise in one or more areas such as deep learning, graph neural networks, or reinforcement learning, with the ability to work in a fast-moving and collaborative environment to deliver broad business impact at scale. Key job responsibilities As an Applied Scientist on the Redshift Query Optimizer team, you will research and develop deep learning models that power resource prediction for one of the world's largest cloud data warehouses. You will take ownership of the end-to-end ML lifecycle, from problem formulation and data analysis to model training, evaluation, and production deployment. You will design novel approaches to understand queries and predict resource needs across diverse and evolving workloads. You will run experiments at scale on real production data, and collaborate closely with systems engineers to deliver low-latency inference in a highly available environment. You will publish your research at top-tier academic venues and contribute to the broader ML-for-systems community. And you will help shape the science roadmap for autonomous database operations while mentoring fellow scientists and engineers. About the team AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

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.