Careers

Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As a Data Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection by applying advanced statistical techniques and machine learning. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Apply new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)

Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As an Applied Scientist I in Traffic Quality, you will solve inherently hard problems in advertising fraud detection using deep learning, self-supervised techniques, representation learning, and advanced clustering. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Deliver on new research problems in fraud detection where neither problem nor solution is well-defined. - Invent and adapt new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Design and deploy production-quality ML components that directly impact advertiser trust and the business top-line. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Contribute to the scientific community through publications at peer-reviewed venues and reviewing research submissions. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge 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. Key job responsibilities 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. Identify and devise new video related solutions following a customer-obsessed scientific approach to address customer or business problems when the problem is ill-defined, needs to be framed, and new methodologies or paradigms need to be invented at the product level. Articulate potential scientific challenges of ongoing or future customers’ needs or business problems, and present interventions to address them. Independently assess alternative video related technologies, driving evaluation and adoption of those that fit best A day in the life As an Applied Scientist on the Sponsored Brands Video team, you will work with a team of talented and experienced engineers, scientists, and designers to help bring new products to market and ensure that our customers are delighted by what we create. The Sponsored Brands Video team is responsible for the design, development, and implementation of Sponsored Brands Video experiences worldwide. About the team The Sponsored Brands Video team within Sponsored Products and Brands creates relevant and engaging video experiences, connecting advertisers and shoppers. We are on a mission to make Amazon the best in class destination for shoppers to discover, engage and build affinity with brands, making shopping delightful, & personal.

The Healthcare AI organization in AWS is seeking an Applied Science Manager to lead science and data teams working on innovative AI-powered healthcare solutions. We are developing innovative products that leverage generative and agentic AI to transform clinical and administrative workflows for healthcare providers. Our aim is to improve patient care, enhance operational efficiency, and deliver exceptional value to our customers. You will lead a team of applied scientists, working on transformative healthcare solutions powered by cloud computing and artificial intelligence. This role offers an opportunity to work at the forefront of applied AI/ML and distributed systems, building new products and services from the ground up. Our team builds on our existing successful healthcare business, which serves major providers and payers in the US. You'll be instrumental in creating solutions that address complex challenges in areas such as clinical documentation, health data analytics, and accurate billing, ultimately contributing to better health outcomes for individuals and populations. Key job responsibilities As an Applied Science Manager, you will be responsible for building and leading a world-class team that pushes the boundaries of computer vision, machine learning, and artificial intelligence. In this role, you will drive the technical vision and strategy for your team while fostering a culture of innovation and scientific excellence. You will balance hands-on technical leadership with people management responsibilities, ensuring your team delivers innovative solutions while growing professionally. The role requires someone who can think strategically about business problems while diving deep into technical details when needed. - Building and mentoring teams of Applied Scientists and ML Engineers - Setting technical direction and research strategy aligned with business goals - Driving innovation in medical administration reasoning systems, AI agent design, and medical coding - Collaborating with cross-functional teams to translate research into production - Managing project portfolios and resource allocation A day in the life Inclusive Team Culture Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign opportunities based on what will help each team member develop into a better-rounded contributor. About the team Our team builds on our existing successful healthcare business, which serves major providers and payers in the US. You'll be instrumental in creating solutions that address complex challenges in areas such as clinical documentation, health data analytics, and accurate billing, ultimately contributing to better health outcomes for individuals and populations.

The Data Intelligence team is a new function within Amazon Customer Service. We own the end-to-end process of defining, building, implementing, and monitoring a comprehensive data strategy. We also develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), Ontology, and Natural Language Processing (NLP) to enhance customer service associate and customer experiences. As an Applied Scientist, you'll own the definition and implementation of customer-focused, AI-driven innovation in Amazon Customer Service globally, leveraging GenAI, ML, and/or NLP to transform complex business requirements and customer needs into innovative technology solutions. Your expertise will be key in shaping data-driven strategies and addressing complex data challenges. With your expertise in AI, text analysis, embeddings, language modeling, and generation, you'll design and develop scalable AI-powered technology solutions, prioritize initiatives, drive data-driven insights, and deliver business impact. This position will advance applied science best practices, leverage data and AI to drive customer experience improvements, and set new global standards for customer experience. This role requires you to work with a cross-functional team, including scientists, engineers, and product managers, to develop scalable and maintainable AI solutions for both structured and unstructured data. The ideal candidate has strong technical skills in AI techniques (e.g., automated reasoning, reasoning, planning, knowledge representation), excellent written documentation skills, and experience with big data technologies. Success in this role requires combining deep business knowledge with hands-on technical skills to solve customer problems and address complex technical challenges. Key job responsibilities - Develop innovative solutions to complex problems (e.g., Automated Reasoning for Trusted AI-Enabled Customer Service). - Apply technical expertise to implement novel algorithms and modeling solutions, in collaboration with other scientists and engineers. - Analyze data and define metrics to identify actionable insights and measure improvements in customer experience. - Communicate results and insights to both technical and non-technical audiences through written reports, presentations, and internal/external publications. - Collaborate with product management and engineering teams to integrate and optimize models in production systems. A day in the life A typical day as an Applied Scientist in the Data Intelligence team involves combining business expertise with hands-on problem-solving in ML and AI. The role encompasses tackling complex data initiatives, ensuring alignment with customer needs and business objectives, and translating business requirements into practical AI-driven solutions. Working collaboratively with cross-functional teams, this position involves designing and enhancing AI models, focusing on efficiency, precision, and scalability. Daily activities include ensuring data quality, monitoring model performance, and generating actionable insights from vast amounts of information. Each day presents opportunities to resolve complex technical challenges, advance important AI projects, and conceive innovative ways to leverage data in transforming the customer experience. About the team The Data Intelligence team is a new function within Amazon Customer Service. We develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), Ontology, and Natural Language Processing (NLP) to enhance customer service associate and customer experiences.

Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced industry leading devices like Fire tablets, Fire TV and Amazon Echo. The Role: As a Senior Design Analysis Engineer, you will be responsible for bringing new product designs through to manufacturing. Structural engineering contributes unique, in-depth technical knowledge to solve complex engineering problems in concert with multi-disciplinary teams including Industrial Design, Hardware Engineering, and Operations. You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will: · Perform analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques · Develop, analyze and test thermal, acoustic and structural solutions; from concept design, feature development, product architecture, through system validation · Support creative developments through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques · Use simulation tools like Abaqus for analysis and design of products · Validate design modifications using simulation and actual prototypes · Use of programming languages like Python and Matlab for analytical/statistical analyses and automation · Establish noise thresholds for usability and compliance requirements · Determine and validate structural performance under use and test conditions · Have strong knowledge of various materials such as heat spreaders solutions to resolve thermal issues, damping materials for noise and vibration suppression · Use various data acquisition systems with thermocouples, accelerometers, strain gauges and IR cameras · Collaborate as part of the device team to iterate and optimize design parameters of enclosures and structural parts to establish and deliver project performance objectives · Design and execute tests using statistical tools to validate analytical models, identify risks and assess design margins · Create and present analytical and experimental results · Develop and apply design guidelines based on project results

Amazon is looking for an Economist - Marketing Science to uncover the impact of Prime Video's Global Marketing efforts, and assess their effects on customer viewership behavior. Prime Video is shaping the future of video entertainment, by offering customers a wide and eclectic catalog, including an ever-increasing slate of Amazon Originals. Our mission is to build the widest selection of digital video content and make it trivially easy for customers to enjoy great content wherever and whenever they want. To help fulfill this mission, the Marketing Science team aims to drive decision-making on Global Prime Video marketing efforts by delivering sophisticated marketing measurement models. As an Economist on the team, you will work closely with our business and finance stakeholders, as well as the other members of our team, to shape and deliver a roadmap of economic models and experimentation for the team. Your frameworks will be leveraged to inform critical decisions for the business, such as 'how much should we invest in marketing globally?', 'what is the value of each $ of marketing activity on each channel?', and 'what is the impact of Brand marketing?'. Key job responsibilities Design and deliver experiments to test critical hypotheses around the effectiveness of Global marketing efforts across different channels. Provide and present analyses that interpret experimental results and leverage them to validate/calibrate the output of observational models. Collaborate with other Scientists and Economists on our team to enhance our existing suite of models and get smarter about marketing decision-making.

The Amazon Search team's vision is to deliver high quality search results regardless of how customers phrase their search queries. Keyword-based search breaks down when confronted with natural language expressions. Queries like "I have ants in my house," "headphones comparable to Bose," "breakfast foods for someone avoiding sugar," and "scratch resistant flooring for dogs that looks like real wood" require world knowledge, common-sense reasoning, and sophisticated language understanding that customers increasingly expect. Core Search team is reimagining search architecture using Large Language Models (LLMs): a new LLM stack that already powers Amazon Search, Alexa+, Alexa for Shopping, Help Me Decide, Interests AI, confidential initiatives, and a growing portfolio of Amazon experiences across Stores and Devices. We build this stack as a primitive to supercharge a new generation of natural-language experiences across Amazon. We are hiring an Applied Scientist to push the science behind this stack: the reasoning LLMs, embedding models, cross-encoder rankers, and multi-objective optimization systems that turn billions of products into the right answer for hundreds of millions of customers. The role spans the full model lifecycle, from mid-training reasoning models on shopping data to aligning the models with customers on the dimensions that matter for shopping: helpfulness, trust, and faithfulness. You will build with us a natural language AI interface to billions of products, for all Amazon customers. Key job responsibilities As an Applied Scientist on the team, you will lead science innovation across multiple problems and surfaces. You will: - Develop personalized multi-modal thinking-LLM techniques that reason about customers, queries, and products. - Mid-train and post-train large language models on shopping data: domain-adaptive continued pre-training, ireinforcement learning shopping reasoning traces, and instruction tuning for natural-language shopping queries. - Align models with customer interests on the dimensions such as helpfulness, harmlessness, and faithfulness. Apply Reinforcement Learning (RLVR, RLHF), Direct Preference Optimization (DPO), and customer-behavior-derived reward models. - Create semantic representations of products, customers, and context (bi-encoder embeddings, contrastive learning, hard-negative mining, cross-lingual training). - Develop cross-attentive LLM rankers that score candidate products against rich query intent and complex constraints. - Train multi-objective ranking and optimization systems that balance relevance, purchasability, and personalization. - Drive improvements on offline benchmarks as well as online experiments. About the team Core Search builds the next-generation LLM-powered retrieval and ranking stack for Amazon. We own the stack end-to-end including LLM models, personalization, multi-turn natural-language refinements, routing, the experimentation service, and the partner-facing primitive that other Amazon teams build on top of. The team is highly motivated, collaborative, technically deep, and runs with strong executive sponsorship and strategic visibility. In this role, you will define program strategy, prioritize investments, and shape how AI-driven natural-language search experiences ship across all devices, globally.

Employer: Amazon Web Services, Inc. Position: Data Scientist II - AMZ27351.1 Location: San Francisco, CA Multiple Positions Available: Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. (40 hours / week, 8:00am-5:00pm, Salary Range $175425 - $212800) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

MULTIPLE POSITIONS AVAILABLE Employer: Amazon Development Center U.S., Inc. Offered Position: Applied Scientist III - AMZ007408 Job Location: New York, NY Position Responsibilities: Participate in the design, development, evaluation, deployment, and updating of formal reasoning systems for security, privacy, and data protection applications. Drive technical and scientific innovation in security automation, data protection, and privacy-preserving technologies, with a focus on developing scalable solutions for cloud environments. Develop and/or apply formal verification techniques and automated theorem proving methods for different applications in cloud security and privacy. Collaborate with internal and external users to understand requirements and enhance formal verification and automated reasoning capabilities. Lead research and development efforts in AI security, specifically evaluate emerging threats and opportunities, including securing Generative AI systems and designing robust safeguards. Proactively identify and explore new opportunities for deploying and leveraging formal reasoning solutions across various domains.