Careers

Are you excited to play a strategic role in defining the customer experience on Tablets? Come join a dynamic, customer-obsessed, world-class science team to completely reshape the customer experience on our tablets. Key job responsibilities Using Amazon’s large-scale computing resources, you will design and deploy state-of-the-art recommendation models. You will ask research questions about customer behavior, design state-of-the-art models that help customers discover content, and deploy these models to production alongside other engineers. You will participate in the Amazon ML community and mentor other engineers with a strong interest in and knowledge of ML and generative AI. Your work will directly benefit customers! A day in the life We are looking for a passionate, hard-working, and talented Applied Scientist who has experience developing state-of-the-art models and deploying them to production. You will have an opportunity to make an enormous impact on the design, architecture, and implementation of products used everyday by real customers! About the team The Amazon Tablet team is a world-class team that delivers high-quality, innovative products that are loved by our customers. Our team focuses on spinning many of Amazon’s flywheels with bar-raising mobile experiences on our tablets. Over the last 12 years, we have built 4.0+ star rated products, and we continue to look for ways to scale our business. Join us as we continue our ambitious journey which will give you the opportunity to deliver disruptive products with global impact.

Join us in the evolution of Amazon’s Seller business! The Selling Partner Selection Succ 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 a Data Scientist II to work on our seller prioritization to improve our SP growth strategy and drive new seller success. As a successful data scientist on our talented team of scientists and economists, you will leverage the latest technology to solve complex problems, and collaborate with engineering, research, and business teams to deliver seller-centric experience on behalf of sour sellers. You need to have deep understanding on the business domain and have the ability to connect business with science. You are also strong in the latest technology and scientific foundation with the ability to collaborate with engineering to put models in production to answer specific business questions. 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 Data Scientist II in the team, you will: - Identify opportunities to improve SP growth and translate those opportunities into science problems via principled GenAI solutions . - 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.

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! Key job responsibilities - Build sequential decision-making frameworks (e.g., MDPs, multi-armed bandits, dynamic programming) to optimize marketing resource allocation over time under uncertainty. - Create predictive models to forecast marketing efficiency and aid strategic budget allocation across channels, campaigns, and markets. - Design and analyze geo-level and regional hold-out experiments to validate model predictions and establish ground truth. - Automate and scale our modeling infrastructure to improve efficiency and expand coverage across use cases and geographies. - Collaborate with leaders across business and finance teams to translate business questions into well-posed statistical and optimization problems. - Quantify uncertainty in model outputs and communicate results, limitations, and recommendations clearly to non-technical stakeholders. About the team The Marketing Science team drives decision-making on Global Prime Video marketing efforts by delivering sophisticated marketing measurement and optimization models. As an Applied Scientist on the team, you will build optimization and forecasting systems that leverage marketing effectiveness insights to deliver actionable investment recommendations. This includes sequential decision-making frameworks that adapt marketing strategy over time as customer behavior evolves. You will work closely with business and finance stakeholders, as well as other members of our science team, to shape and deliver a roadmap of models. Your frameworks will inform critical decisions for the business.

Employer: Amazon Development Center U.S., Inc. Position: Applied Scientist III - AMZ27579.1 Location: New York, NY Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. Telecommuting may be permitted. (40 hours / week, 8:00am-5:00pm, Salary Range $183800 - $248700) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

As part of AI Ops Integration team we have a vision to transform Amazon Operations & Supply Chain into an AI-Native organization by delivering intuitive and differentiated AI solutions that solve enduring operational challenges. We blend vision with curiosity and Amazon's real-world experience to build rapidly AI capabilities. We accelerate our customers' businesses: internal operations teams across Amazon's global footprint through delivery of predictive analytics, LLMs and autonomous AI agents to automate decision-making across Amazon's global operations and supply chain. As an Applied Scientist you will combine state-of-the-art ML/AI techniques with emerging Agentic AI capabilities to build autonomous systems that can understand, reason about, and optimize complex supply chain operations. You will also have the opportunity to work on CV and other relevant technologies. This role combines the excitement of a startup environment with the scale of Amazon Operations. You'll research state-of-the-art open source and internal tools and will tackle highly ambiguous problems. If you thrive on ownership and dealing with ambiguity, passionate about AI and want to fundamentally influence how Amazon Operations leverages AI, this role offers an extraordinary opportunity to make your mark. Key job responsibilities - Lead the development of innovative ML and AI solutions for supply chain and operations - Rapidly prototype and validate new ideas through minimum lovable products (MLPs) - Transform promising prototypes into production-ready systems at global scale - Collaborate with business stakeholders to identify and prioritize high-value automation opportunities - Mentor junior members and promote engineering excellence in AI/ML practices About the team We work back to back to address the technical challenges of automation and intelligence across a variety of products, software, and systems. Our scientists and machine learning engineers work in synergy to solve hard problems and enrich each other's skills. Together, we are a team of worldwide specialists in bringing the potential of practical ML and AI to the max with impact on millions of Amazon customers.

The Models, Quantum, and Silicon (MQS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire a Research Software Engineer to join our growing Software team. You will work closely with our experimental physics teams to enable their work characterizing, calibrating, and operating novel quantum devices. The ideal candidate should be able to translate high-level science requirements into software implementations (e.g. Python APIs/frameworks, data analysis pipelines, calibration nodes) that are performant, scalable, and intuitive. This requires someone who (1) has a strong desire to work within a team of scientists and engineers, and (2) demonstrates ownership in initiating and driving projects to completion. This role has a particular emphasis on working directly with experimental physicists to develop scientific software workflows that enable scaling to larger quantum devices. Inclusive Team Culture Here at Amazon, 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. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the 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. 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. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility. Key job responsibilities - Architect extensible & intuitive frameworks for running quantum computing experiments and analyzing data. - Leverage the latest techniques in quantum calibration to enable scaling to larger devices. - Optimize the performance of experiment & analysis tools to enable faster experiment throughput. - Develop dashboards that allow experimentalists to inspect and control the state of quantum device calibration. - Deploy and maintain cloud infrastructure that supports increasingly-complex science workflows. - Empower scientists to actively contribute to the codebase through mentorship and documentation. We are looking for candidates with strong engineering principles, a bias for action, superior problem-solving, and excellent communication skills. Working effectively within a team environment is essential. As a Research Software Engineer embedded in a broader research science organization, you will have the opportunity to work on new ideas and stay abreast of the field of experimental quantum computation. A day in the life The majority of your time will be spent on projects that extend the functional capabilities or performance of our internal research software stack. This requires working backwards from the needs of our science staff in the context of our larger experimental roadmap. You will translate science and software requirements into design proposals balancing implementation complexity against time-to-delivery. Once a design proposal has been reviewed and accepted, you’ll drive implementation and coordinate with internal stakeholders to ensure a smooth roll out. Because many high-level experimental goals have cross-cutting requirements, you’ll often work closely with other engineers or scientists or on the team. About the team You will be joining the Software group within the MQS Center of Quantum Computing. Our team is comprised of scientists and software engineers who are building scalable software that enables quantum computing technologies.

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. The Data, Insights, Science and Optimization, Music Product and Tech (DISCO M&G) team is looking for a Data Scientist to join a team of scientists and engineers who analyze big data, provide analytics and insights and build models and algorithms to power Music product experiences. In this role, you will set the science vision and direction for the team and collaborate with internal stakeholders across marketing tech, Product , science and finance to scale and advance our science offerings. You will lead large scale science solutions, prioritize across multiple stakeholders and projects and be part of a fast-paced, dynamic and fun environment. Key job responsibilities • Lead the research and development of models and science products powering customer identity, acquisition, and retention strategies for Amazon Music • Partner with Marketing and Growth leadership to develop science-driven business strategies, including subscriber segmentation, lifecycle management, and upsell programs • Build predictive models — including propensity, uplift, and causal inference models — to support targeted marketing initiatives, measure incremental impact, and automate deep dives into key metric variances • Collaborate with product, engineering, and marketing teams to evaluate the impact of growth campaigns, features, and algorithmic interventions through rigorous experimentation • Analyze experiment results and provide data-driven recommendations to optimize subscriber acquisition and retention outcomes • Partner with Senior Data Scientists and Product Managers to define, propose, and validate success and guardrail metrics for identity and growth programs • Educate marketing and growth teams on analytics, measurement frameworks, and experimentation best practices, encouraging a culture of evidence-based decision making About the team The DISCO team focuses on accelerating Amazon Music customer growth by empowering product teams to make sound, customer-centric decisions through data and insights. We build data pipelines, self-service analytics, insights and predictive models enabling acquisition, engagement and retention at scale with personalized customer touch points.

Amazon's Middle Mile Surface Research Science seeks an Applied Scientist to invent and build machine learning models that improve how Amazon plans and operates its transportation network. Amazon's transportation network moves millions of truckloads of freight between vendors, warehouses, and customers using a fleet of trucks, trains, and airplanes, on time and at low cost. Operating it requires constant decisions about how to route, schedule, and balance capacity across the network, and our strategy is to make those decisions with science-driven technology. Because existing techniques rarely fit Amazon's scale and unique business needs, this role centers on inventing new approaches and algorithms. As an Applied Scientist, you'll develop machine learning, forecasting, and prediction models and algorithms. Your first project will focus on trailer imbalance forecasting and safety stock optimization to improve our equipment re-balancing strategy, where you'll own the prediction models and grow your scope over time. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. Key job responsibilities - Design and develop machine learning, forecasting, and other prediction models that enhance our optimization and planning systems. - Build models and algorithms from prototype to production-level systems. - Translate ambiguous business problems into modeling approaches, and drive the technical design with product, engineering, and operations partners. - Influence key business decisions through rigorous modeling and analysis. - Communicate results and recommendations to scientific and business audiences. A day in the life - Analyze data to investigate a business problem or model performance and identify improvements - Brainstorm new algorithmic strategies or business opportunities with fellow scientists - Leverage GenAI to build and test your new model features - Run a simulation or experiment to evaluate your model’s performance - Meet with product and tech partners to review project requirements, data, design, or other project decisions - Review code changes or a design document from a fellow scientist or engineers - Write and present a paper documenting algorithm features, results, and recommendations About the team Middle Mile Surface Research Science builds the models and algorithms that plan and operate Amazon's middle mile network. Our work spans operations research, optimization, and machine learning. We work on vehicle route planning, capacity planning, scheduling, network design, transit-time prediction, demand forecasting, and equipment re-balancing. Our team of about ten scientists is part of a broader science organization whose scientists bring deep expertise in machine learning and optimization. We optimize decisions worth billions of dollars and reach millions of customers.

Amazon's Middle Mile Surface Research Science seeks an Applied Scientist to invent and build machine learning models that improve how Amazon plans and operates its transportation network. Amazon's transportation network moves millions of truckloads of freight between vendors, warehouses, and customers using a fleet of trucks, trains, and airplanes, on time and at low cost. Operating it requires constant decisions about how to route, schedule, and balance capacity across the network, and our strategy is to make those decisions with science-driven technology. Because existing techniques rarely fit Amazon's scale and unique business needs, this role centers on inventing new approaches and algorithms. As an Applied Scientist, you'll develop machine learning, forecasting, and prediction models and algorithms. You role will initially develop transit time prediction and uncertainty models. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. Key job responsibilities - Design and develop machine learning, forecasting, and other prediction models that enhance our optimization and planning systems. - Build models and algorithms from prototype to production-level systems. - Translate ambiguous business problems into modeling approaches, and drive the technical design with product, engineering, and operations partners. - Influence key business decisions through rigorous modeling and analysis. - Communicate results and recommendations to scientific and business audiences. A day in the life - Analyze data to investigate a business problem or model performance and identify improvements - Brainstorm new algorithmic strategies or business opportunities with fellow scientists - Leverage GenAI to build and test your new model features - Run a simulation or experiment to evaluate your model’s performance - Meet with product and tech partners to review project requirements, data, design, or other project decisions - Review code changes or a design document from a fellow scientist or engineers - Write and present a paper documenting algorithm features, results, and recommendations About the team Middle Mile Surface Research Science builds the models and algorithms that plan and operate Amazon's middle mile network. Our work spans operations research, optimization, and machine learning. We work on vehicle route planning, capacity planning, scheduling, network design, transit-time prediction, demand forecasting, and equipment re-balancing. Our team of about ten scientists is part of a broader science organization whose scientists bring deep expertise in machine learning and optimization. We optimize decisions worth billions of dollars and reach millions of customers.

Amazon's Middle Mile Surface Research Science seeks an Applied Scientist to invent and build optimization models and algorithm to improve how Amazon plans and operates its transportation network. Amazon's transportation network moves millions of truckloads of freight between vendors, warehouses, and customers using a fleet of trucks, trains, and airplanes, on time and at low cost. Operating it requires constant decisions about how to route, schedule, and balance capacity across the network, and our strategy is to make those decisions with science-driven technology. Because existing techniques rarely fit Amazon's scale and unique business needs, this role centers on inventing new approaches and algorithms. As an Applied Scientist, you'll develop optimization models and algorithms. Your role will initially focus on driver capacity optimization. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. Key job responsibilities - Design and develop optimization models and algorithms that enhance our optimization and planning systems. - Build models and algorithms from prototype to production-level systems. - Translate ambiguous business problems into modeling approaches, and drive the technical design with product, engineering, and operations partners. - Influence key business decisions through rigorous modeling and analysis. - Communicate results and recommendations to scientific and business audiences. A day in the life - Analyze data to investigate a business problem or model performance and identify improvements - Brainstorm new algorithmic strategies or business opportunities with fellow scientists - Leverage GenAI to build and test your new model features - Run a simulation or experiment to evaluate your model’s performance - Meet with product and tech partners to review project requirements, data, design, or other project decisions - Review code changes or a design document from a fellow scientist or engineers - Write and present a paper documenting algorithm features, results, and recommendations About the team Middle Mile Surface Research Science builds the models and algorithms that plan and operate Amazon's middle mile network. Our work spans operations research, optimization, and machine learning. We work on vehicle route planning, capacity planning, scheduling, network design, transit-time prediction, demand forecasting, and equipment re-balancing. Our team of about ten scientists is part of a broader science organization whose scientists bring deep expertise in machine learning and optimization. We optimize decisions worth billions of dollars and reach millions of customers.