Careers

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, Consumer Product and Tech (DISCO CPT) team is looking for a Data Scientist to join a team of Data Scientists, Analytics Leads, Business Intelligence Engineers and Data Engineers who analyze big data, provide analytics and insights as well as build models and algorithms that power product for personalized experiences. The 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, science models, self-service analytics, insights and dashboards, deliver engagement and retention at scale. In this role, you will pioneer and execute advanced analytics and experimentation efforts for the Amazon Music product team. You will work with stakeholders and partners to deliver experimentation insights, author scientific artifacts, build scalable models to advance our analytics and science products. The ideal candidate must be willing to effectively lead science solutions, prioritize across multiple stakeholders and projects and be ready to jump into a fast-paced, dynamic and fun environment. Key job responsibilities - Data Scientist II on this role will be a thought partner to Product and Engineering organizations through their expertise in experimentation, statistical modeling and advanced analytics - Conceptualize and drive strategic growth areas: retention, voice + visual engagement, experimentation, Gen AI initiatives, and product excellence. - Bridge science, analytics, and engineering to deliver solutions informing strategic decisions on AM technology investments and long-term business performance - Drive experimentation, measurement efficiency tooling, and ROI tracking for AM leadership - Build and maintain causal models to understand customer sentiment and its impact on key engagement/retention metrics - Develop structural and predictive models using data science workflows to deliver measurable customer results A day in the life - Lead strategic initiatives across visual and voice experiences, experimentation and customer retention stakeholders - Conduct experimentation, build causal models to better understand key drivers for customer retention - Evaluate existing metrics and lead deep dives to ensure the right measurement framework is leveraged to measure and enhance user adoption through visual and voice interfaces - Partner with product managers to translate business questions into actionable science projects - Influence product roadmaps through data-driven insights on customer behavior patterns - Identify and resolve customer experience friction points with cross-functional teams - Drive adoption of best practices and innovative solutions across the organization

We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their causal inference and/or structural econometrics skillsets to solve real world problems. The intern will work in the area of Ads Marketing and develop models to understand differential impacts of changing promotional ads credits on sellers and advertiser behavior. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. About the team The Amazon Advertising finance department works closely with the business teams to analyze advertiser insights, validate financial results of investments and advertising product growth initiatives, and drives controllership across the organization. In this role, your goal is to understand the impact of changing ads credits on sellers and advertisers behavior (e.g. acquisition, retention, growth, etc.) as well as using this information to optimize our credit program and maximizing long term-value.

The Amazon Web Services Center for Quantum Computing (CQC) in Pasadena, CA, is looking to hire an Applied Scientist. You will join a multi-disciplinary team of experimental and theoretical physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have a solid foundation in experimental physics and deep and broad knowledge of experimental measurement techniques. 

Candidates should have a track record of original scientific contributions, and those with deep knowledge and experience in low temperature condensed matter physics (superconductivity, Josephson junctions, phononics, TLS physics etc.) will be preferred. We are looking for candidates with strong engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. Working effectively within a team environment is essential. You will be expected to work on new ideas and stay abreast of the field of experimental quantum computation. Key job responsibilities In this role, you will be part of the Physical Metrology and Exploration (PME) subgroup within the Device team. You will work with the basic building blocks of quantum processors, ranging from superconducting films and resonators to junctions and qubits. There are two core missions for the PME team. The first is to support the CQC’s central activities through the development of devices, both existing and novel, that fall within this complexity range. This involves understanding their physical principles, identifying performance limitations, and collaboratively enhancing their capabilities. In many cases though, existing tools and measurement protocols fall short in delivering the depth of insights required for this first mission. Hence the second core mission of the team centers on physical metrology development. This involves continuously refining existing methodologies and simultaneously devising entirely new ones by identifying specific physical phenomena and exploiting them for measurement applications. You will constantly learn, innovate, carry out agile and yet scientifically rigorous research, and disseminate findings to the rest of the organization. You will be responsible for building experiments that encompass the integrated stack: design, fabrication, cryogenics, signal chain, and control stack software. Based on your research, you will provide recommendations that improve our next-generation quantum processors.

We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their causal inference skillsets to solve real world problems. The intern will work in the area of Amazon Ads measurement and incrementality and develop models to quantify bias between observational and causal conversion path rate metrics. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. Key job responsibilities Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis.

We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their causal inference skillsets to solve real world problems. The intern will work in the area of AWS and develop models to understand customer service usage. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis.

Amazon's Worldwide Pricing & Promotions organization is seeking a strong Sr. Applied Scientist to help solve complex business problems involving promotion sourcing algorithms and promotion merchandising strategies at a global scale. This Sr. Applied Scientist will operate in a team of other scientists and economists. Our team applies causal inferences, statistics, machine learning, forecasting, optimization, economics, and experimentation to drive actionable insights and to improve strategic business decision-making. This is an individual contributor role that requires collaboration across teams and functions to solve core business problems for the company around setting promotional strategies. The work is part of significant scientific investments in promotions systems that forecast customer demand, predicts the quality of promotions, and optimize promotions sourcing and merchandising strategies across different surfaces. Key job responsibilities * Identify and devise new research solutions to address complex problems related to promotion forecasting and optimization * Lead the design, implementation, and successful delivery of solutions for scientifically-complex problems and systems in production, which can be brand new, or evolving from existing ones * Apply and drive the team to adopt best practices * Influence your team’s science and business strategy by making insightful contributions to team roadmaps, goals, priorities, and approach * Serve as a role model for publishing research results at internal and external venues, when appropriate * Proactively communicate your ideas effectively to achieve the right outcome for your team and customer * Actively participate in the science hiring process as well as mentor other scientists - improving their skills, their knowledge of your solutions, and their ability to get things done A day in the life As a Sr. Applied Scientist on the WW Pricing & Promotions Science team, you are considered a leader in your team. You are recognized for your expertise, and your peers regularly seek your advice. You invent and design new solutions for scientifically-complex problem areas to improve the promotions business. You apply and set the example for best practices in applied science and software engineering, and systematically peer review code written by your team members. You drive your team’s scientific agenda by proposing new initiatives and securing management buy-in. You author internal documents and/or external publications where appropriate. About the team The WW Pricing & Promotions Science team is responsible for driving scientific innovation to support pricing and promotions programs across Amazon's businesses. We specialize in experimental and observational causal methods, forecasting, and optimization. We apply these tools to drive business decision making at scale, leading to launch decisions of new pricing algorithms and new promotion strategies, understanding short- and long-term value of different programs, and the prioritization of budget allocations. We also develop models to set optimal prices and promotions, and define innovative price guardrails and incentives to optimize for long-term program health.

Amazon's Community Intelligence Science & Engineering (CISE) team operates at the intersection of computational social science, artificial intelligence, and operational planning. We build and validate models that quantify how Amazon's operational presence impacts local communities, and embed those signals into core planning models that drive Last Mile, Middle Mile, and site-level decisions across our network. You'll work at the forefront of applied AI and causal inference, building systems that influence decisions affecting thousands of communities daily. This role offers a unique opportunity to shape how the world's most customer-centric company measures, forecasts, and reduces operational risk to the communities we serve. A successful Senior Research Scientist on our team demonstrates exceptional scientific rigor combined with pragmatic execution. You will design the causal frameworks and predictive models that systematically embed these community signals into core operational planning processes where they do not yet exist. This means balancing breakthrough research with production deployment, collaborating across multiple science organizations, and translating complex analytical findings into actionable insights that influence senior leadership strategy. As a Senior Research Scientist, you will partner with product teams, operational scientists, and technical leaders across Last Mile Planning Science, Middle Mile Relay, Safety, and Worldwide Operations Security to identify opportunities where community intelligence creates multiplicative business impact. Key job responsibilities - Collaborate with operational science teams to integrate community risk signals into existing operational models and decision-making systems, with a focus on quantifying performance lift and defining integration architecture - Design and execute experiments to measure how community-impacting operational policies affect business outcomes - Build automated causal discovery systems leveraging knowledge graphs, LLMs, and document understanding to uncover relationships between operational policies and community outcomes - Design and deploy production ML forecasting systems with extended prediction horizons using multi-modal data sources, including survey-based indices, geospatial risk features, and operational metrics - Mentor junior scientists and contribute to building a research culture that balances high-risk, high-reward innovation with reliable product delivery About the team Amazon Community Operations (CommOps) works to anticipate our communities’ needs and build positive net impact anywhere we operate. From Operational Excellence to Community Outreach, Community Operations is defining what it means to be a positive presence in the community. This is a fast-paced, start-up environment where creative problem solving, operational excellence, and building relationships are the core of our day-to-day work.

The Amazon Q Developer Science team is looking for an Applied Scientist who is passionate about building services and tools for developers that leverage artificial intelligence (AI) agents and machine learning (ML). You will be part of a team building AI-based services for Amazon Q Developer with the focus on redefining the way developer work. The team works in close collaboration with other AWS AI services such as AWS Bedrock, the AWS IDE Toolkit, and Amazon Sagemaker. If you are excited about working in cloud computing and building new AWS services, then we'd love to talk to you. Key job responsibilities As a senior Applied Scientist, you are recognized for your expertise, advise team members on a range of machine learning topics, and work closely with software engineers to drive the delivery of end-to-end modeling solutions. Your work focuses on ambiguous problem areas where the business problem or opportunity may not yet be defined. The problems that you take on require scientific breakthroughs. You take a long-term view of the business objectives, product roadmaps, technologies, and how they should evolve. You drive mindful discussions with customers, engineers, and scientist peers. You bring perspective and provide context for current technology choices, and make recommendations on the right modeling and component design approach to achieve the desired customer experience and business outcome. About the team 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. Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. 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 (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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. Mentorship and 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. Diverse Experiences Amazon 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.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. We are the Sponsored Products - Marketplace Intelligence (MI) team. We are looking for an Applied Scientist to help build production ML and bandit solutions to customize the search experience. We determine which ads to show in Amazon search, where to place them, how many ads to place, and to which customers. This helps shoppers discover new products while helping advertisers put their products in front of the right customers, aligning shoppers’, advertisers’, and Amazon’s interests. To do this, we apply a broad range of machine learning, causal inference, and optimization techniques to continuously explore, learn, and optimize the allocation and ranking of ads on the search page. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the SP experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. You will be on the Search Ad Ranking and Interleaving team org - specifically the team that focusses on whole page optimization. Our mission is to personalize and contextualize SP ad allocation on the entire search page. We do this by modeling shopper responses to the number, placement, and quality of ads. We are a data- and hypothesis-driven organization that uses online experimentation, simulation, causal modeling, and online feedback to place ads where they’re useful to shoppers and provide improved discoverability and sales for advertisers. This is a unique opportunity for someone who wants to have broad business impact, a direct impact on customers and the search experience, and get broad exposure to a wide range of scientific techniques (machine learning, bandit learning, optimization, LLMs). We are looking for an Applied Scientist to join Interleaving team in Marketplace Intelligence with a broad mandate to experiment and innovate to grow Sponsored Products. We’d like someone with practical experience with LLMs / GenAI for production to improve how we rank and allocate ads on the page today. If you thrive in a product-focussed and data-driven environment, then this role is for you. As a Applied Scientist on this team, you will help to identify unique opportunities to create customized and delightful shopping experience for our growing marketplaces worldwide. Your job will be to identify big opportunities for the team that can help to grow Sponsored Products business working with retail partner teams, product managers, software engineers and TPMs. You will have opportunity to design, run and analyze / experiments to improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact. More importantly, you will have the opportunity to broaden your technical skills in an environment that thrives on creativity, experimentation, and product innovation. Key job responsibilities * Tackle and solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. * Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. * Develop efficient algorithms for multi-objective optimization and AI control methods to find operating points for the ad marketplace then evolve them * Be an expert at designing and implementing solutions that use a range of data science methodologies to automate data analysis or to solve complex business problems. * Perform hands-on analysis and modeling of enormous data sets to develop insights that improve shopper experience, without compromising Ad revenue in addition to designing metrics for complex systems. * Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity. * Run A/B experiments, gather data, and perform statistical analysis.

The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.