Careers

Amazon launched the AGI Lab to develop foundational capabilities for useful AI agents. We built Nova Act - a new AI model trained to perform actions within a web browser. The team builds AI/ML infrastructure that powers our production systems to run performantly at high scale. We’re also enabling practical AI to make our customers more productive, empowered, and fulfilled. In particular, our work combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments. Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. We’d love for you to join our lab and build it from the ground up! Key job responsibilities This role will lead a team of SDEs building AI agents infrastructure from launch to scale. The role requires the ability to span across ML/AI system architecture and infrastructure. You will work closely with application developers and scientists to have a impact on the Agentic AI industry. We're looking for a Software Development Manager who is energized by building high performance systems, making an impact and thrives in fast-paced, collaborative environments. About the team Check out the Nova Act tools our team built on on nova.amazon.com/act

Are you interested in building the measurement foundation that proves whether targeted, cohort-based marketing actually changes customer behavior at Amazon scale? We are seeking an Applied Scientist to own measurement and experimentation for our Lifecycle Marketing Experimentation roadmap within the PRIMAS (Prime & Marketing Analytics and Science) team. In this role, you will design and execute rigorous experiments that measure the effectiveness of audience-based marketing campaigns across multiple channels, providing the evidence that guides marketing strategy and investment decisions. This is a high-impact role where you will build measurement frameworks from scratch, design experiments that isolate causal effects, and establish the experimental standards for lifecycle marketing across EU. You will work closely with business leaders and the senior science lead to answer critical questions: does targeting specific cohorts (Bargain hunters, Young adults) improve efficiency vs. broad campaigns? Which creative strategies drive behavior change? How should we optimize marketing spend across channels? Key job responsibilities Measurement & Experimentation Ownership: 1. Own measurement end-to-end for lifecycle marketing campaigns – design experiments (RCTs, geo-tests, audience holdouts) that measure campaign effectiveness across marketing channels 2. Build measurement frameworks and experimental best practices that work across different activation platforms and can scale to multiple campaigns 3. Establish experimental standards and tooling for lifecycle marketing, ensuring statistical rigor while balancing business constraints Causal Inference & Analysis: 1. Apply causal inference methods to measure incremental impact of marketing campaigns vs. counterfactual 2. Navigate measurement challenges across different platforms (Meta attribution, LiveRamp, clean rooms, onsite tracking) 3. Analyze experiment results and provide optimization recommendations based on statistical evidence 4. Establish guardrails and success criteria for campaign evaluation About the team The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.

Amazon is seeking passionate, talented and inventive Applied Scientists with a strong machine learning background to help build Agentic AI solutions for Selling Partners and Retail users. Our mission is to elevate their experience through intelligent, collaborative Agentic AI solution powered by specialized agents that deliver seamless, personalized support at scale. Key job responsibilities As part of our AERO team in Amazon PGX, you will work alongside internationally recognized experts to develop novel solutions and modeling techniques to advance the state-of-the-art in Generative and Agentic AI. Your work will directly impact millions of our users in the form of products and services that improves their experience. You will gain hands on experience with Amazon’s native/partnered Large Language Models (LLMs), technologies like AgentCore, AgentMemory, Strands and Model Context Protocol (MCPs). We are also looking for talents with experiences/expertise in building large-scale and high-performing systems.

Lead ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Sr Research Scientist, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.

Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. We're seeking an Applied Scientist to join our Robotics team. This role focuses on developing innovative machine learning solutions that enable robots to perform complex manipulation tasks in real-world environments. You will work on creating adaptive learning approaches that combine traditional robotics with modern ML techniques to improve robot performance and reliability. In this role, you will collaborate with multidisciplinary teams to advance the state-of-the-art in robotic manipulation, contributing to the development of next-generation autonomous systems that can operate safely and efficiently within Amazon fulfillment centers. Key job responsibilities - Lead design, adapt, and implement novel machine learning solutions for manipulation robots - Create hybrid approaches combining classical methods with learning-based solutions - Design learning algorithms for automated parameter tuning and adaptation - Develop data collection pipelines and methodologies for capturing high-quality demonstrations of dexterous tasks - Build and test prototype robotic workcell setups to validate the performance of the solution - Partner with cross-functional teams to rapidly create new concepts and prototypes - Work with Amazon's robotics engineering and operations teams to grasp their requirements and develop tailored solutions - Document the architecture, performance, and validation of the final system

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through generative and agentic 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 to transform every aspect of the advertising lifecycle; from ad creation, delivery, optimization, performance management, and beyond. We are a passionate group of innovators dedicated to developing state-of-the-art AI technologies that balance the needs of advertisers and enhance the shopping experience. Within SPB, the SPB Offsite (SPBO) team builds solutions to extend campaigns to reach customers off the store and extend shopping experiences on third-party sites where shoppers search and discover products. We use industry-leading machine learning, high-scale low-latency systems, and gen AI technologies to create better sponsored customer experiences off the store. The Principal Applied Scientist for SPBO leads the technical vision and scientific strategy for extending Amazon Advertising's sponsored experiences to the broader web—meeting shoppers wherever they search, browse, and discover products. This is a multi-disciplinary scientific space spanning machine learning, large-scale optimization, causal inference, NLP, information retrieval, and generative AI. You will define and drive the science roadmap for how Amazon connects advertisers with high-intent customers across third-party environments at massive scale and with low latency. As a GenAI-first organization, we build foundational and agentic models that power advertiser use cases across Ads, while empowering our Applied Scientists to directly build and ship products. You will be a hands-on technical leader who architects novel solutions end-to-end—from research through production—while mentoring a team of scientists across diverse domains. The problems you will tackle are among the hardest in ad tech. You will develop models that leverage Amazon's first-party shopping signals to reach high-value audiences in third-party environments where signal density differs fundamentally from on-Amazon contexts. You will innovate on real-time bidding, auction dynamics, and ranking models across heterogeneous supply sources with distinct inventory characteristics, latency constraints, and auction mechanics. You will design ML approaches that maintain effectiveness amid an evolving privacy landscape—turning constraints from cookie deprecation, regulation, and platform restrictions into innovation opportunities. You will influence attribution models that capture the incremental value of offsite advertising on shopping outcomes, bridging measurement gaps between offsite touchpoints and on-Amazon conversions. You will pioneer generative and agentic AI to personalize ad creatives and shopping experiences for offsite contexts, and develop scientific frameworks to optimize spend allocation across supply partners and channels. You will partner with engineering, product, and business leaders as well as external partners to shape product strategy with scientific insight and drive results at scale. You will represent Amazon Advertising's offsite science externally through patents and industry engagement. Key job responsibilities - Driving the scientific vision of the teams in your organization and advising and influencing its technical leadership on ad serving, bidding, ranking, and offsite advertising models and products. - Identifying, tackling, and proposing innovative solutions to intrinsically hard, previously unsolved problems in offsite ad tech. - Bringing clarity to complex problems, probing assumptions, illuminating pitfalls, fostering shared understanding, and guiding towards effective solutions. - Serving and being recognized by internal and external peers as a thought leader in offsite advertising science, including real-time bidding, personalization, privacy-preserving ML, and generative AI for ad experiences. - Influencing your team's science and business strategy by driving one or more team roadmaps contributing to the organization's roadmap and taking responsibility for some organizational goals. You drive multiple new product features from inception to production launch. - Guiding the career development of others, actively mentoring and educating the larger applied science community on trends, technologies, and best practices.

At Amazon, we are committed to being the Earth's most customer-centric company. The European International Technology group (EU INTech) owns the enhancement and delivery of Amazon's engineering to all the varied customers and cultures of the world. We do this through a combination of partnerships with other Amazon technical teams and our own innovative new projects. You will be joining the Tamale team to work on Haul. As part of EU INTech and Haul, Tamale strives to create a discovery-driven shopping experience using challenging machine learning and ranking solutions. You will be exposed to large-scale recommendation systems, multi-objective optimization, and state-of-the-art deep learning architectures, and you'll be part of a key effort to improve our customers' browsing experience by building next-generation ranking models for Amazon Haul's endless scroll experience. We are looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading ranking solutions. We strongly value your hard work and obsession to solve complex problems on behalf of Amazon customers. Key job responsibilities We look for applied scientists who possess a wide variety of skills. As the successful applicant for this role, you will work closely with your business partners to identify opportunities for innovation. You will apply machine learning solutions to optimize multi-objective ranking, improve discovery engagement through contextual signals, and scale ranking systems across multiple marketplaces. You will work with business leaders, scientists, and product managers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed ranking services. You will be part of a team of scientists and engineers working on solving ranking and personalization challenges at scale. You will be able to influence the scientific roadmap of the team, setting the standards for scientific excellence. You will be working with state-of-the-art architectures and real-time feature serving systems. Your work will improve the experience of millions of daily customers using Amazon Haul worldwide. You will have the chance to have great customer impact and continue growing in one of the most innovative companies in the world. You will learn a huge amount - and have a lot of fun - in the process!

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques

Are you interested in defining the science strategy that enables Amazon to market to millions of customers based on their lifecycle needs rather than one-size-fits-all campaigns? We are seeking a Applied Scientist to lead the science strategy for our Lifecycle Marketing Experimentation roadmap within the PRIMAS (Prime & Marketing analytics and science) team. The position is open to candidates in Amsterdam and Barcelona. In this role, you will own the end-to-end science approach that enables EU marketing to shift from broad, generic campaigns to targeted, cohort-based marketing that changes customer behavior. This is a high-ambiguity, high-impact role where you will define what problems are worth solving, build the science foundation from scratch, and influence senior business leaders on marketing strategy. You will work directly with Business Directors and channel leaders to solve critical business problems: how do we win back customers lost to competitors, convert Young Adults to Prime, and optimize marketing spend by de-averaging across customer cohorts. Key job responsibilities Science Strategy & Leadership: 1. Own the end-to-end science strategy for lifecycle marketing, defining the roadmap across audience targeting, behavioral modeling, and measurement 2. Navigate high ambiguity in defining customer journey frameworks and behavioral models – our most challenging science problem with no established playbook 3. Lead strategic discussions with business leaders translating business needs into science solutions and building trust across business and tech partners 4. Mentor and guide a team of 2-3 scientists and BIEs on technical execution while contributing hands-on to the hardest problems Advanced Customer Behavior Modeling: 1. Build sophisticated propensity models identifying customer cohorts based on lifecycle stage and complex behavioral patterns (e.g., Bargain hunters, Young adults Prime prospects) 2. Define customer journey frameworks using advanced techniques (Hidden Markov Models, sequential decision-making) to model how customers transition across lifecycle stages 3. Identify which customer behaviors and triggers drive lifecycle progression and what messaging/levers are most effective for each cohort 4. Integrate 1P behavioral data with 2P survey insights to create rich, actionable audience definitions Measurement & Cross-Workstream Integration: 1. Partner with measurement scientist to design experiments (RCTs) that isolate audience targeting effects from creative effects 2. Ensure audience definitions, journey models, and measurement frameworks work coherently across Meta, LiveRamp, and owned channels 3. Establish feedback loops connecting measurement insights back to model improvements About the team The PRIMAS (Prime & Marketing Analytics and Science) is the team that support the science & analytics needs of the EU Prime and Marketing organization, an org that supports the Prime and Marketing programs in European marketplaces and comprises 250-300 employees. The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.

This role is on the Core Tech Private Brands Analytics (PBA) team, a cross-functional team (software engineering, data science, data engineering, business intelligence) that owns Amazon Private Brands (APBs) central data infrastructure and builds platforms and models that help improve business performance. In this job you will build and improve forecasting and planning models across APB, partnering with business, science, and tech stakeholders. Day-to-day work includes end-to-end pipeline development (feature engineering through training and deployment) on SageMaker, S3, and Datanet, replacing manual spreadsheet-driven processes with reproducible code-driven pipelines and dashboards, evaluating model accuracy across business segments, and contributing to APB's science standards alongside a senior scientist assessing the org's AI framework and experimentation rigor. Key job responsibilities The ideal candidate has strong fundamentals in forecasting and applied ML, experience with Python and SQL, comfort working with large-scale retail datasets, and the ability to communicate findings clearly to non-technical partners.