Careers

Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (audio, images, videos, text) by building AI-driven solutions for advertisers. To accomplish this, we are investing in understanding how best users can leverage Generative AI methods such as latent-diffusion models, large language models (LLM), generative audio (music and speech synthesis), computer vision (CV), reinforced learning (RL) and related. As an Applied Scientist you will be part of a close-knit team of other applied scientists, engineers and product managers who are highly collaborative and at the top of their respective fields. We are looking for talented Applied Scientists who are adept at a variety of skills, especially at the interface of how users can use state-of-the-art generative music and audio, computer vision, latent diffusion or related foundational models that will accelerate our plans to generate high-quality creatives on behalf of advertisers. Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, publish, patent, and bring innovative research to raise the bar within the team. You will: - Drive the invention and development of novel methods for the use of Generative AI in ads - Work closely and integrate end-to-end proof-of-concept Machine Learning projects that have a high degree of ambiguity, scale and complexity. - Build interface-oriented systems that use Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Mentor and help recruit Applied Scientists to the team. - Present results and explain methods to senior leadership. - Willingness to publish research at internal and external top scientific venues. - Write and pursue IP submissions. Key job responsibilities This role is focused on developing Machine Learning and Generative AI methods to augment generative imagery and videos. You will develop new paradigms that will impact how users can engage and benefit from GenAI and that are at the core of advertising-facing tools that we are launching. You will also work on development of ML and GenAI models. You will conduct literature reviews to stay on the top of the field. You will regularly engage with product managers, who will partner with you to productize your work. About the team

We are seeking an Applied Scientist to develop and optimize Visual Inertial Odometry (VIO) and sensor fusion systems for our intelligent robots. In this role, you will design, implement, and deploy state estimation and tracking algorithms that enable robots to understand their position and motion in real time, even in challenging and dynamic environments. You will own the full pipeline from algorithm development through embedded deployment, ensuring that perception systems run efficiently on resource-constrained robotic hardware. You will also leverage modern machine learning approaches to push the boundaries of classical perception methods, combining learned representations with geometric techniques to achieve robust, real-time performance. This is a deeply hands-on role. You will work directly with sensors, hardware, and real-world data, while prototyping, testing, and iterating in physical environments. The ideal candidate has strong foundations in VIO and sensor fusion, practical experience optimizing algorithms for embedded platforms, and familiarity with how modern deep learning is transforming perception. Key job responsibilities - Design and implement Visual Inertial Odometry algorithms for robust real-time state estimation on robotic platforms like Sprout - Develop multi-sensor fusion pipelines integrating cameras, IMUs, and other sensing modalities for accurate pose tracking - Optimize perception and tracking algorithms for deployment on embedded hardware (e.g., ARM, GPU-accelerated edge devices) under strict latency and power constraints - Apply modern ML-based perception techniques (learned features, depth estimation, neural odometry) to complement and improve classical geometric approaches - Build and maintain calibration, evaluation, and benchmarking infrastructure for perception systems - Collaborate with hardware, controls, and navigation teams to integrate perception outputs into the robot’s autonomy stack - Lead technical projects from research prototyping through production deployment

At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our mission is to prevent denied entities from transacting with Amazon businesses. We build automatic mechanisms to detect and prevent prohibited transactions with denied entities using a diverse set of algorithms and machine learning techniques. We screen over a billion events every day and develop algorithms which are able to scale and detect suspicious entities . We are still Day 1 and have an exciting road map to build Machine Learning (ML) and Generative AI (LLM) powered detection and resolution systems to help scale Amazon for years to come. We are seeking an Applied Scientist to join our team and help tackle challenging problems at the forefront of machine learning and artificial intelligence. Working closely with a multidisciplinary team of engineers, data scientists, and domain experts, you will play a crucial role in defining innovative ML/AI-powered customer experiences and solutions. If you have an entrepreneurial mindset, the technical depth to deliver impactful results, and a passion for innovation, we want to hear from you. Key job responsibilities In this role, you will: • Drive the research, design, and development of novel ML/AI models and systems to power critical products and services • Collaborate cross-functionally to deeply understand business requirements, customer needs, and technical constraints • Rapidly prototype, test, and iterate on ML/AI solutions, iterating quickly based on data and feedback • Communicate complex technical concepts to technical and non-technical stakeholders • Stay up-to-date on the latest advancements in AI/ML and identify opportunities to apply emerging techniques A day in the life - Starting the day by reviewing the latest model performance metrics and identifying areas for improvement - Brainstorming new architectures and approaches with your cross-functional team during a whiteboard session - Diving deep into a complex dataset, leveraging advanced statistical and AI techniques to uncover hidden insights - Prototyping a new model and running a series of experiments to optimize its performance - Preparing a presentation to pitch your latest research findings and recommendations to product and engineering leaders - Mentoring junior scientists, providing guidance on coding best practices and problem-solving strategies About the team Why Amazon Security At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training 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, training, and other career-advancing resources here to help you develop into a better-rounded professional.

We are seeking a Data Scientist II with strong applied-science skills to join our Perfect Order Experience (POE) team. In this role you will build models that anticipate customer, seller, brand, and program outcomes, working at the intersection of predictive modeling, economic reasoning, and business strategy. You will partner closely with business stakeholders to translate complex, open-ended problems into well-defined scientific questions, and develop predictive models that turn those questions into actionable insight. Your work will directly inform how the business grows, protects customers and brands, and makes investment and policy decisions.

Do you want to build the ML models that power every compliance verdict customers and auditors rely on? We are building a new Governance, Risk, and Compliance (GRC) service at AWS where every action — human or agent — is recorded, assessed, and provably governed, continuously and at any scale. As an Applied Scientist on this team, you will own the Natural Language Inference (NLI) model at the center of our compliance engine: fine-tuning, calibrating, and monitoring it in production. You will also design the science behind framework decomposition, evidence auto-linking, and a new agent-context system that tackles retrieval, grounding, and hallucination measurement for AI agents. Your work will directly shape the accuracy and trustworthiness of every customer-visible result we produce. Key job responsibilities - Own the production NLI model end-to-end: fine-tune, promote, calibrate thresholds, and monitor for drift to ensure accurate compliance verdicts across frameworks. - Design and implement framework decomposition pipelines that translate compliance controls into NLI hypotheses, including enrichment and per-framework threshold calibration. - Build and improve the evidence auto-linking system using ranking, embedding, and reranking techniques, tuning precision-recall tradeoffs and addressing OCR quality challenges. - Drive the quality flywheel by establishing labeling standards, designing offline and online evaluation methods, orchestrating retraining cycles, and ensuring privacy-safe data handling. - Research and develop the agent-context capability — retrieval and representation of lifecycle signals, cross-framework knowledge, memory, grounding, and hallucination measurement — collaborating with engineering pods to bring these models into production. A day in the life You will split your time between collaborative and heads-down work. On the collaborative side, you will partner with several pods of software development engineers in an Agile cadence — aligning on data collection needs, sharing experiment results, and integrating models into production services. On the heads-down side, you will dig into model training runs, analyze evaluation metrics, and iterate on threshold tuning until the numbers meet the bar. Your work is roadmap-driven, giving you the space to plan experiments methodically while still responding to new framework onboarding requests as they arise. About the team Our team builds the AI and ML systems behind AWS Governance, Risk, & Compliance service, where science is not auxiliary — it is the core. Every customer-visible compliance result flows through models our scientists create and maintain. We are investing in next-generation capabilities including observability and governance for AI agents, and the multi-quarter Agent Context initiative is an organization-defining program that will need technical leaders. Growth here is tangible. You will own the full model lifecycle from training through production monitoring, develop rare expertise at the intersection of ML and compliance, and have a clear path to expand your scope into technical leadership as the service scales.

Amazon Prime is looking for an ambitious Economist to help create econometric insights for world-wide Prime. Prime is Amazon's premiere membership program, with over 200M members world-wide. This role is at the center of many major company decisions that impact Amazon's customers. These decisions span a variety of industries, each reflecting the diversity of Prime benefits. These range from fast-free e-commerce shipping, digital content (e.g., exclusive streaming video, music, gaming, photos), reading, healthcare, and grocery offerings. Prime Science creates insights that power these decisions. As an economist in this role, you will create statistical tools that embed causal interpretations. You will utilize massive data, state-of-the-art scientific computing, econometrics (causal, counterfactual/structural, experimentation), and machine-learning, to do so. Some of the science you create will be publishable in internal or external scientific journals and conferences. You will work closely with a team of economists, applied scientists, data professionals (business analysts, business intelligence engineers), product managers, and software/data engineers. You will create insights from descriptive statistics, as well as from novel statistical and econometric models. You will create internal-to-Amazon-facing automated scientific data products to power company decisions. You will write strategic documents explaining how senior company leaders should utilize these insights to create sustainable value for customers. These leaders will often include the senior-most leaders at Amazon. The team is unique in its exposure to company-wide strategies as well as senior leadership. It operates at the research frontier of utilizing data, econometrics, artificial intelligence, and machine-learning to form business strategies. A successful candidate will have demonstrated a capacity for building, estimating, and defending statistical models (e.g., causal, counterfactual, machine-learning) using software such as R, Python, or STATA. They will have a willingness to learn and apply a broad set of statistical and computational techniques to supplement deep training in one area of econometrics. For example, many applications on the team motivate the use of structural econometrics and machine-learning. They rely on building scalable production software, which involves a broad set of world-class software-building skills often learned on-the-job. As a consequence, already-obtained knowledge of SQL, machine learning, and large-scale scientific computing using distributed computing infrastructures such as Spark-Scala or PySpark would be a plus. Additionally, this candidate will show a track-record of delivering projects well and on-time, preferably in collaboration with other team members (e.g. co-authors). Candidates must have very strong writing and emotional intelligence skills (for collaborative teamwork, often with colleagues in different functional roles), a growth mindset, and a capacity for dealing with a high-level of ambiguity. Endowed with these traits and on-the-job-growth, the role will provide the opportunity to have a large strategic, world-wide impact on the customer experiences of Prime members. About the team The Prime Economics organization is a cross-functional group of scientists (economists, research scientists, applied scientists), business intelligence engineers, software engineers and product managers embedded within Prime.

The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows. As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money." This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. In this role you will - Own the science vision for training models in our RL Gym from end-to-end - Invent models for joint budget allocation, incrementality, and long-term value - Take innovations from prototype to production serving live advertiser campaigns - Raise the technical bar across scientists and engineers Key job responsibilities - Define the long-term scientific vision for our RL Gym based training process focusing on campaign optimization and other advertising workflows - Translating ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues.

Are you passionate about transforming complex technical challenges into elegant, automated solutions that empower customers to leverage innovative technology? Do you thrive on building highly scalable systems that process massive amounts of data and deliver actionable insights through advanced analytics, machine learning, and AI assisted learning? Our mission: Every action - human or agent is recorded, assessed, and provably governed. Continuously and anywhere. Automatically. At any scale. We are looking for a strong Applied Scientist for new features and services that will allow our customers to fine-tune Natural Language Inference (NLI) model, framework decomposition, and evidence auto-linking that produce every customer-visible compliance verdict. About the team Our team is leading AI/ML research and development in the security and governance space. We use state-of-the-art science and engineering practices to provide continuous compliance and risk analysis, and we move fast to implement solutions and refine them based on customer feedback. Our team is adding additional focus on next-generation capabilities including AI-assisted development workflows, observability/governance for AI agents, and advanced analytics. This role will allow you to drive executive alignment and technical innovation.

Core to Amazon's DNA is its methodology around executing experiments and prototypes to find the quickest way to prove new ideas, technologies, and business models in a real-world environment. Experimentation fuels Amazon's innovation. Our team does horizon exploration. We take emerging technology, including our own inventions, build working prototypes with customers and partners, and demonstrate what becomes possible before the rest of the market gets there. Prototyping is how we do the work. Pushing the technology horizon is why we do it. If you are a highly creative problem solver who works to inspire, motivate, and define innovative solutions; are passionate about technology, understand cloud architectures and platforms, and are quick to pick up emerging technologies; are adept at working with customers to experiment with innovative approaches and validate the technical feasibility of solutions, this could be the role for you. As a Senior Applied Scientist you sit directly with customers and partners. You help them envision complex uses of AWS by building working prototypes of specific use cases, tailored to their data, devices, and systems. Engagements run in short build cycles, typically four to six weeks, and are designed to be innovative and evaluative, demonstrating the art of the possible on AWS while proving a credible path to production. You will work across the areas where we are placing our biggest bets. These shift as the technology does, but today they include: - Physical AI and autonomous robotics. World models, vision-language-action (VLA) models, cloud-based and agentic VLA, real-to-sim and sim-to-real pipelines, and agentic control of physical systems. We challenge the assumption that these models have to live only at the edge. - Autonomous scientific discovery. Self-driving labs and instrument intelligence, where agents plan, run, and interpret experiments across fleets of scientific instruments. - Agentic developer intelligence. Multi-agent systems, code agents, and automated research workflows that change how software gets built. - Scaling what we learn. Turning one-off prototypes into reusable patterns and assets the broader field can pick up and run with. You will not just apply these techniques. You will help decide which bets are worth making and prove or disprove them in the open with real customer problems. Key job responsibilities - As a key member of the AWS Specialists and Partner Prototyping team, ensure customer success through hands-on participation producing and demonstrating prototypes on the AWS platform. - Participate in deep architectural and scientific design discussions to ensure prototypes are crafted for successful deployment in the cloud and a credible path to production. - Bring applied science depth to engagements: frame the problem, select and adapt models, design experiments, and judge technical feasibility honestly, including when the answer is that an approach will not work. - Conduct one-to-few and one-to-many training sessions to transfer knowledge to prototyping customers, partners, and internal business partners. - Act as a thought leader for your area of domain expertise across AWS, and share best-practice knowledge among the community. - Author or otherwise contribute to AWS customer-facing publications such as peer reviewed position papers, blogs, and reference patterns. - Act as a technical liaison between customers, service engineering teams, and sales, carrying real-world signal back to the teams building the services. A day in the life No two engagements look the same, which is the point. A typical week might include: - Running a working session with a customer's scientists and engineers to pressure-test an idea and agree on what a prototype needs to prove. - Writing code and training or fine-tuning models in the lab, often against the customer's own data, devices, or robots. - Pairing with a prototyping SA and engagement manager, the standard pod you deliver alongside, to keep the build moving and the scope honest. - Demonstrating a working prototype live and walking stakeholders through what it does, what it does not yet do, and what production would take. - Capturing what you learned as a reusable pattern or publication so the next team does not start from zero. - Spending time on the horizon itself: reading, experimenting with a new model or technique, and forming a point of view on where a bet is heading. You will move between hands-on building and customer-facing explanation constantly. The best people in this role are equally comfortable in a research paper, a code editor, and a room full of executives. 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) 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.

Amazon Advertising operates at a scale few places in the world can match, and every ad shown to a customer must meet a high bar for trust and quality. We are looking for an Applied Scientist II to advance the machine learning systems that evaluate advertising content at scale — balancing accuracy, latency, and cost so that we protect the customer experience without slowing advertisers down. You will own meaningful scientific problems end to end: framing the problem, building and validating models, and taking them to production where they make decisions on live traffic. This is a hands-on science role with real ownership. You will work across large multilingual datasets, modern ML and large language model techniques, and tight engineering constraints, partnering closely with other scientists, engineers, and product teams to turn research into measurable customer impact. Key job responsibilities - Design, build, and ship ML models and systems that classify and evaluate content accurately and efficiently at scale. - Translate ambiguous business and policy problems into well-scoped scientific problems with clear success metrics. - Apply and adapt modern techniques — including deep learning, representation learning, and large/small language models — to production problems under real latency and cost constraints. - Own the full model lifecycle: data, training, evaluation, deployment, monitoring, and retraining in response to drift and changing requirements. - Define evaluation methodology and the metrics that matter (precision, recall, cost of error, latency), and defend trade-offs with evidence. - Collaborate with engineering and product partners to integrate models into live systems, and mentor junior scientists.