Careers

Amazon Ads Brand Safety & Suitability protects advertisers from exposure to unsafe, unsuitable, or policy-violating content across web, mobile app, CTV, and audio advertising inventory. Our mission is to ensure that every ad impression delivered through Amazon's demand-side platform appears adjacent to content that meets advertiser trust expectations while giving brands granular controls to define suitability on their own terms. We operate at the intersection of advertiser trust, publisher quality, and supply integrity. AI is fundamentally changing the content landscape. Content is now generated at unprecedented scale — faster, cheaper, and increasingly sophisticated. Low-quality, deceptive, AI-generated, and synthetic content evolves in real time, constantly adapting to evade detection. The volume and velocity of new content entering the advertising system has outpaced traditional classification approaches. We are looking for an Applied Scientist to work on the next generation of AI-powered Brand Safety and Content Classification systems designed to protect advertisers and elevate supply quality at internet scale. This is not a traditional classification problem. You will build systems that make millisecond-level decisions across billions of content signals while continuously adapting to emerging content risks driven by generative AI. You will own the science strategy for LLM-powered classification and semantic understanding, real-time multimodal content evaluation, adversarial ML and adaptive model resilience, proactive risk intelligence and content risk hunting, AI-generated and synthetic content detection, and large-scale abusive content system identification and disruption. You will define how modern AI separates high-quality advertising inventory from unsafe, unsuitable, and policy-violating content — across web, mobile app, CTV, and audio surfaces. What Makes This Role Unique Generative AI has dramatically lowered the cost of producing deceptive, policy-evasive content, and the adversary evolves daily. Your detection systems must reason contextually, adapt rapidly, and generalize beyond previously seen content risk patterns. Static models fail here; you will build living systems that learn and respond in real time. You will do this at internet scale, developing low-latency ML and LLM-powered systems evaluating content safety, brand suitability, misinformation risk, and emerging content risk vectors across massive real-time traffic streams, making billions of decisions per day with single-digit millisecond latency constraints. This role sits at the intersection of frontier AI research and large-scale production engineering, combining deep science, system-wide impact, and business-critical outcomes. The models your team ships directly influence billions of dollars in advertising spend and the trust of the world's largest brands in Amazon DSP. The Science Problems Are Genuinely Hard You will tackle challenges including detecting sophisticated AI-generated and synthetic content, understanding nuanced contextual brand risk, identifying coordinated MFA space before they scale, balancing precision, recall, latency, explainability, and fairness, designing adaptive models resilient to adversarial evolution, and leveraging LLMs for semantic understanding in real-time, latency-constrained environments. Why This Matters Few roles offer the opportunity to work at the intersection of frontier AI, internet-scale production systems, adversarial environments, and business-critical impact — while tackling open-ended scientific challenges with real-world societal relevance. As AI reshapes the internet, the systems your team builds will define what trustworthy, high-quality digital systems look like for the next decade. Key job responsibilities - Own the science strategy for AI-powered brand safety classification. - Build LLM-powered content classification systems making billions of decisions/day at single-digit millisecond latency - Develop multimodal evaluation pipelines reasoning across text, images, audio, and video in real time - Design adaptive ML systems resilient to adversarial evolution, semantic understanding for nuanced contextual brand risk. - Define measurement frameworks and drive continuous improvement - Translate research into production — own the path from prototype to deployed model - Publish at peer-reviewed venues; contribute to the scientific community in adversarial ML, NLP, and content safety - Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems.

AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making. * Define and implement new automated reasoning features that employ scalable and efficient approaches to solve complex problems using neural learning and symbolic/formal reasoning * Apply software engineering best practices to ensure a high standard of quality for all team deliverables * Work in an agile, startup-like development environment * Deliver high-quality scientific artifacts * Work with the team to help drive business decisions About the team Why AWS? AWS is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues

The Prime Video Science team leverages the latest in machine learning and AI techniques combined with causal inference to bring scientific rigor to the biggest decisions in entertainment: what content to make, what to license, and where to invest. We build large-scale models that simulate how our global customer base responds to change, and we get to see that work shape what the business does. Prime Video is an industry-leading entertainment business and a critical driver of Amazon Prime subscriptions, contributing to customer loyalty and lifetime value. We're looking for a Data Scientist to help us design the experiments that ground our models in reality, evaluate the AI systems we build, and turn our model and experiment results into insights the business can act on. As a Data Scientist on this team, you will design and analyze experiments, build evaluations for AI systems, and run deep-dive analyses on our models, experiments, and customer data. You will work close to science: probing why a model behaves the way it does, pressure-testing results before they reach senior leaders, and, where it helps, building your own statistical and causal models. The candidate should have strong communication skills and the ability to translate complex, ambiguous analyses into clear findings for both technical and business audiences. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and the ability to work in a fast-paced and ever-changing environment. Key job responsibilities • Design and analyze randomized experiments that validate and calibrate our models and measure the impact of content and product changes. • Build evaluations and benchmarks for the AI systems the team develops and define what "good" looks like for them. • Run deep-dive analyses on model outputs, experiment results, and customer behavior to surface the story behind the numbers and catch issues before they reach stakeholders. • Apply statistical modeling, causal inference, and data analysis to answer business questions and inform major investment decisions. • Communicate findings to business, finance, engineering, and science stakeholders through clear written analyses and business-facing documents. About the team The Prime Video Science team is a multidisciplinary group of applied scientists, data scientists, economists, and engineers. We take on some of the hardest research questions in the business, and our work carries visibility up to the CFO/CEO level. We pursue ambitious research at the intersection of machine learning, AI, and causal inference, turning that research into innovations that improve customer experience and strengthen business profitability. Few science teams get to work on problems this hard and this impactful; if that combination excites you, we'd love to talk.

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 subscriptions such as Apple TV+, HBO Max, Peacock, 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 team member, 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! As an Applied Scientist, you will apply state of the art natural language processing and computer vision research to video centric digital media. We are looking for scientists with expertise in vision-language models/multimodal LLMs and long-form content understanding (full movies/episode vs. short clips). You will be dealing with architectures that handle long-context understanding and causal reasoning across extended temporal sequences. Key job responsibilities Our team builds multi-modal machine learning technologies to enrich and understand video content. We aim not only to understand individual components within the content itself, but also their relationships to each other to provide a holistic and broader contextual understanding. This powers the next generation of video understanding and search capabilities for Prime Video. About the team Prime Video's Content Localization, Understanding & Enrichment organization is responsible for 1) enabling Prime Video to "see" and "understand" video content including characters, scenes, dialogue, events & visual elements and 2) delivering localized, accessible content that meets a consistent cinematic quality standard at scale. This team's mission is to deeply understand all content and empower all customers with relevant language options, innovative accessibility assists, and rich title-information across all their content-experiences on Prime Video. We create and publish content on-time that's meaningful, accurate, and accessible to every customer globally. We delight our customers by pushing the boundaries of content understanding and enrichment. Through inclusion and innovation, we do the most fulfilling work of our career.

Are you passionate about data, enjoy solving complex analytical problems, leveraging industry leading agentic AI technologies to derive insight at scale - all in a challenging, fast-paced environment? We are seeking an Applied Scientist to accelerate the growth of Amazon Internal Audit’s Data Science & Risk Intelligence initiatives. The team builds ML and AI solutions that expand self-service data utilization by audit teams, utilizing the right methods to derive deeper patterns, and surface insights to gain holistic perspectives while amplifying potential risk mitigation. Key job responsibilities - Work with audit teams, product managers, engineers, and more senior scientists to deliver machine learning and generative AI products that carry real degrees of ambiguity, scale, and complexity. - Design, build, and evaluate agentic AI systems — multi-agent workflows, retrieval-augmented generation, and tool-using agents — that automate and augment audit work, applying rigorous LLM-as-judge and human-aligned evaluation to measure and improve output quality. - Apply statistical analysis and classical machine learning using SQL and scripting languages like Python/R over large datasets to develop insights and recommendations that strengthen internal audit. - Architect secure, scalable solutions on AWS machine learning and generative AI services (e.g., Bedrock, AgentCore, SageMaker), owning the full lifecycle from prototype through production deployment, monitoring, and iterative improvement, working closely with auditors to understand their business needs. - Build and maintain the team's production and experimentation infrastructure, including deployment pipelines, observability and tracing, and evaluation harnesses. - Advance applied research by exploring emerging techniques and sharing findings through internal and external publications, talks, and conferences. A day in the life As an Applied Scientist, you will help shape and execute a product roadmap that connects risk to the business, building AI products — increasingly centered on large language models and agentic systems — that make audit work more effective and efficient. Your work spans the full arc of applied science: framing ambiguous problems, prototyping with the latest generative AI techniques, building rigorous evaluations, and deploying solutions in production. The ideal candidate pairs a strong foundation in data science and machine learning with a builder's instinct for production architecture, thrives on ambiguity, and stays close to a fast-moving research frontier. About the team Internal Audit’s mission is to help our businesses improve controllership, operational efficiency, and customer experience.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist III Job Location: Santa Clara, California Job Number: AMZ9976173 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. 40 hours / week, 8:00am-5:00pm, Salary Range: $183,000/year to $247,600/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000

We are looking for a Principal Applied Scientist to drive the research and development of real-time multimodal conversational AI. You will operate across two focus areas: advancing foundation models for speech and audio, and building the post-training systems (reward modeling, reinforcement learning) that shape natural, human-like conversational behavior. You will be the expert in your area while contributing across the full model lifecycle — from pre-training and architecture design through post-training alignment and real-time deployment. You will work at the frontier of what's possible in conversational AI, with the compute, data, and runway to pursue problems that few teams in the world have the resources to tackle. As a Principal Scientist, you will set the technical direction for your research area, influence the broader roadmap, and work closely with inference engineers to ensure your models are designed for real-time production deployment from inception. Key job responsibilities Foundation Model Scaling - Build and train large-scale multimodal foundation models for real-time speech and audio generation, from architecture design through production-scale training - Advance the scaling and efficiency of conversational modes, including the relationship between data, model size, and real time performance. - Design model architectures informed by hardware constraints and inference requirements, working with inference engineers to ensure models are servable from inception - Develop training methodologies for multimodal models that jointly process and generate speech, language, and audio in real-time streaming contexts Post-Training & Reinforcement Learning - Design and build reward models and reward functions for speech systems — capturing naturalness, fluency, conversational quality, and real-time responsiveness - Develop and apply reinforcement learning methods to shape conversational behavior — teaching models natural timing, responsiveness, and fluid interaction - Build the post-training pipeline from SFT through RL alignment, optimized for real-time multimodal outputs rather than text-only generation - Design evaluation frameworks that capture the quality dimensions unique to real-time conversation Real-Time Perception & Generation - Advance the team's capabilities in real-time perception - Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets

The Amazon Fulfillment Technologies (AFT) Science team is looking for an exceptional Applied Scientist, with strong optimization and analytical skills, to develop production solutions for one of the most complex systems in the world: Amazon’s Fulfillment Network. At AFT Science, we design, build and deploy optimization, simulation, and machine learning solutions to power the production systems running at world wide Amazon Fulfillment Centers. We solve a wide range of problems that are encountered in the network, including labor planning and staffing, demand prioritization, pick assignment and scheduling, and flow process optimization. We are tasked to develop innovative, scalable, and reliable science-driven solutions that are beyond the published state of art in order to run frequently (ranging from every few minutes to every few hours per use case) and continuously in our large scale network. Key job responsibilities As an Applied Scientist, you will work with other scientists, software engineers, product managers, and operations leaders to develop scientific solutions and analytics using a variety of tools and observe direct impact to process efficiency and associate experience in the fulfillment network. Key responsibilities include: * Develop an understanding and domain knowledge of operational processes, system architecture and functions, and business requirements * Deep dive into data and code to identify opportunities for continuous improvement and/or disruptive new approach * Develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and new challenges * Create prototypes and simulations for agile experimentation of devised solutions * Advocate technical solutions to business stakeholders, engineering teams, and senior leadership * Partner with engineers to integrate prototypes into production systems * Design experiment to test new or incremental solutions launched in production and build metrics to track performance A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Amazon Fulfillment Technology (AFT) designs, develops and operates the end-to-end fulfillment technology solutions for all Amazon Fulfillment Centers (FC). We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. The AFT Science team has expertise in operations research, optimization, scheduling, planning, simulation, and machine learning. We also have domain expertise in the operational processes within the FCs and their defects. We prioritize advancements that support AFT tech teams and focus areas rather than specific fields of research or individual business partners. We influence each stage of innovation from inception to deployment which includes both developing novel solutions or improving existing approaches. Resulting production systems rely on a diverse set of technologies, our teams therefore invest in multiple specialties as the needs of each focus area evolves.

We're looking for a senior scientist to lead the research direction for a system that gives AI persistent, compounding memory. This is a new problem space — not recommendation, not search, not summarization, though it draws from all three. The right scientist will define what this field becomes. You'll own the scientific roadmap, run a research agenda with real-world deployment targets, and mentor junior scientists. The team is forming now. Your first week will involve scoping experiments, not reading onboarding docs. Key job responsibilities As a Senior Applied Scientist, you will own the scientific roadmap for personalization initiatives, identifying high-impact research directions and translating ambiguous problems into well-defined ML formulations. You will lead end-to-end systems spanning knowledge acquisition, retrieval, and reasoning. Specific responsibilities include: 1. Define the scientific roadmap for knowledge acquisition, representation, and retrieval at organizational scale. 2. Lead research on how AI systems should learn from experience — what to capture, how to generalize, when to forget. 3. Design evaluation frameworks for a system where "quality" means something new — right knowledge, right context, right confidence level. 4. Own end-to-end research from problem formulation through production impact measurement. 5. Mentor Applied Scientists and establish scientific standards for a new team. 6. Partner with engineering leadership to translate research into architecture decisions that shape the product. 7. Drive technical decisions on model architecture, training methodology, and evaluation frameworks, balancing scientific rigor with business impact. 8. Publish at top-tier venues and advance the state of the art in applied knowledge systems. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, execute experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the organization. You get to influence stakeholders with clear communication skills. You innovate on behalf of the customer and strategically build features. You will mentor junior members and help them grow. About the team Born out of Amazon's Personalization organization, which pioneered personalization at internet scale. We're applying deep expertise in large-scale ML to a fundamentally new domain where the signal space, objective functions, and evaluation criteria are all open research questions. The team values innovation and offers a safe place to try, fail, and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. We offer creative space with an entrepreneurial work environment focusing on customer obsession.