Careers

Join the AWS Perimeter Protection team as a Senior Applied Scientist, where you will bring your deep ML engineering expertise to design, build, and scale AI-driven security solutions that protect AWS customers worldwide. This role is ideal for someone who has already built and shipped production ML systems at industry scale and is looking to apply that experience to high-impact security challenges. You will own the full ML lifecycle — from research and prototyping to production deployment and optimization — powering services including Web Application Firewall, DDoS Protection, Bot Management, and Infrastructure Protection. With services spanning all AWS regions and handling trillions of requests per week, you will solve complex engineering and science problems where model performance, system reliability, and low-latency inference are critical. Key job responsibilities - Develop and implement advanced AI/ML models and algorithms to enhance the capabilities of our security services, enabling proactive threat detection, mitigation, and protection against evolving cyber threats. - Collaborate with security engineers and researchers, to understand business/domain requirements, analyze data patterns, and translate insights into actionable solutions. - Design and drive implementation of data pipelines and ETL processes to ingest, process, and analyze large-scale security data from multiple sources, ensuring data quality and integrity. - Conduct in-depth data analysis, feature engineering, and model evaluation to continuously improve the performance and accuracy of AI/ML-based security solutions. - Participate in the development and deployment of AI/ML models into production environments, ensuring scalability, reliability, and performance at cloud scale. - Collaborate with cross-functional teams to ensure seamless integration of AI/ML solutions with our security services and infrastructure. - Contribute to the development of best practices, documentation, and knowledge-sharing within the team and the broader organization. - Engage in research and exploration of emerging technologies and techniques relevant to AI/ML-based security solutions, stay up-to-date with the latest trends cybersecurity, and incorporate new techniques and methodologies into our security offerings.

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

Do you want to transform the way people shop at Amazon? We are looking for stellar applied scientists to be part of our multi-disciplinary team. We are re-imagining the future of the Amazon shopping experience for customers through tailoring it to their current intent. We understand what customers are interested in shopping for, and guide them to discover what they need as they shop. If you love building new technology that helps customers solve their problems, this is your opportunity to impact millions of customers. Come help us build the future of personalized shopping at Amazon! As an Applied Scientist on the team, you will work on science innovation in our space across a large multidisciplinary team. You will have a breadth of problems to work on, ranging from developing state of the art LLM-based techniques to reason about customers and products, developing deep learned transformer-based models to understand and abstract customer intent signals and representations, building large-scale real-time multi-task ranking systems, and more. You will build technology employed by teams across the company, while also having a direct connection to millions of customers through our own customer facing features every day. Come join us in the journey! Key job responsibilities Key job responsibilities * Deliver new features and models that have huge impact on the customer experience. Help customers find the right products and content on their shopping journey. Leverage the use of advanced machine learning to create customer shopping experience at Amazon's scale - for all Amazon customers across all countries in realtime * Be a key contributor on a multidisciplinary team across science, product, design, and engineering to see through ideas from inception, prototype, to launch in the hands of all Amazon's customers * Propose and innovate on the science roadmap across multiple projects About the team Our vision is to build the next generation of shopping experience at Amazon through personalization and understanding the customer's intent. We imagine our core experiences to work together as a talented personal shopping assistant would — a partner that is knowledgeable, understands your preferences, and helps you find the right solution for your needs. We aim for the quality of personalization to be a core reason customers choose Amazon, on par with Earth’s largest selection, low prices, and fast and free shipping. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. We run global experiments and our work has revolutionized e-commerce with features such as "Keep shopping for ...", “Customers who bought this item also bought”, and “Frequently bought together”. Amazon’s internal surveys regularly recognize us as one of the best engineering organizations to work for in the company, with visible high-impact work, low operational load, respectful work-life balance, and continual opportunity to learn and grow.

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.

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.

The AOP (Analytics Operations and Programs) team is responsible for creating core analytics, insight generation and science capabilities for ROW Ops. We develop scalable analytics applications, AI/ML products and research models to optimize operation processes. You will work with Product Managers, Data Engineers, Data Scientists, Research Scientists, Applied Scientists and Business Intelligence Engineers using rigorous quantitative approaches to ensure high quality data/science products for our customers around the world. As a Data Scientist, you will play a crucial role in supporting the team by creating and maintaining the data infrastructure necessary for the advanced analytics and machine learning solutions. Our team solves a broad range of problems that can be scaled across ROW (Rest of the World including countries like India, Australia, Singapore, MENA and LATAM). Here is a glimpse of the problems that this team deals with on a regular basis: • Using live package and truck signals to adjust truck capacities in real-time • HOTW models for Last Mile Channel Allocation • Using LLMs to automate analytical processes and insight generation • Ops research to optimize middle mile truck routes • Working with global partner science teams to affect Reinforcement Learning based pricing models and estimating Shipments Per Route for $MM savings • Deep Learning models to synthesize attributes of addresses • Abuse detection models to reduce network losses Key job responsibilities 1. AI Agent development for data analytics. 2. Analyze data with statistical and ML techniques. 3. Develop analysis/model in scripting languages (e.g. Python, R) and statistical/mathematical software (e.g. SAS, Matlab, etc.). 4. Develop science-based Supply Chain solutions. 5. Analysis/model documentation. 6. Learn and understand state-of-the-art statistical and ML techniques/tools. 7. Learn and understand Amazon Supply Chain operations.

Amazon's Worldwide Grocery Stores (WWGS), Data & Science team is seeking an Applied Scientist to join our under the roof (UTR) Science team, focused on improving outbound pick efficiencies across the Amazon Grocery Network. In this role, you will build optimization and simulation models that directly reduce operational costs and improve associate productivity in warehouse picking operations. This role owns the development and deployment of mathematical optimization models for pick planning, inventory placement, and warehouse layout design. You will formulate ambiguous business problems as concrete scientific models, develop and deploy production-grade solutions, and work closely with engineering partners, product owners, and business stakeholders to deliver measurable impact. Because UTR operations are complex and inter-connected (e.g., inbound stow vs. outbound pick), this role requires a strong understanding of these relationships and the ability to make trade-offs at the system level. You will interface directly with non-technical product owners and business leaders, manage expectations, and take an active part in influencing the feature roadmap. Key job responsibilities - Design, develop, and deploy mathematical optimization models (e.g., Mixed Integer Programming, meta-heuristics) to improve outbound picking efficiency, including pick planning and inventory placement. - Build simulation models to evaluate warehouse layout designs, test optimization solutions offline, and answer strategic what-if questions. - Formulate complex, ambiguous business problems into well-defined scientific solutions with clear objectives and constraints. - Collaborate with engineering teams to productionize models, establish data pipelines, and create scalable architectures. - Track solution performance post-deployment, identify issues through deep dives, and iteratively improve model quality. - Communicate technical concepts clearly to diverse stakeholders — scientists, engineers, product managers, and business leaders — through documentation, presentations, and design reviews. - Author peer-reviewed research papers on developed models and contribute to the internal scientific community.

Are you a PhD interested in machine learning, natural language processing, computer vision, automated reasoning, robotics, or quantum technologies? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Key job responsibilities Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.

In this role, you will design and build intelligent multi-agent systems that automate root cause analysis for advertising campaign delivery at scale. You will architect agentic orchestration patterns where specialized sub-agents (campaign diagnostics, deal-level troubleshooting, pacing control) are invoked as composable tools by a reasoning layer that determines which subsystems to query based on the nature of the issue. You will develop hierarchical analysis frameworks that move from daily trend detection to intra-day anomaly isolation, enabling the system to pinpoint when and why delivery degraded rather than relying on static time windows. You will build self-learning feedback loops where the system identifies recurring failure signatures (auction dynamics, pacing anomalies, supply contention), updates its diagnostic knowledge as engineering teams deploy fixes, and retires stale patterns automatically. We are looking for a passionate Applied Scientist with technical expertise in LLM-based agent architectures, retrieval-augmented generation, time-series anomaly detection, and production ML systems. In addition to hands-on experience building agentic AI solutions, an ideal candidate should demonstrate the ability to translate complex distributed system behaviors into structured diagnostic reasoning, show a willingness to push the boundaries of how LLMs interact with real-time operational data, and thrive in an environment where you ship production systems that directly reduce advertiser escalation time from days to minutes. Key job responsibilities * Conduct deep data analysis to derive insights for the business, identify gaps, and uncover new opportunities. * Develop scalable and effective machine learning models and optimization strategies to solve business problems. * Run regular A/B experiments, gather data, and perform statistical analysis to optimize advertiser experiences. * Collaborate closely with software engineers to deliver end-to-end solutions into production. * Enhance the scalability, efficiency, and automation of large-scale data analytics, model training, deployment, and serving. * Research and implement new machine learning models and techniques to improve advertising performance. A day in the life Your primary focus is building a multi-agent diagnostic system that automates root cause analysis for advertising campaign delivery issues. On a typical day, you might review how the system handled recent escalations, identify where it reasoned incorrectly, adjust orchestration logic, and write new evaluation cases. You will design agent architectures that invoke specialized sub-agents as tools, build hierarchical analysis frameworks that move from trend detection to anomaly isolation, and develop self-learning loops that keep the system's diagnostic knowledge current as the underlying platform evolves. You will work closely with SDEs building the diagnostic platform, product managers defining the troubleshooting experience, and the support teams who rely on your system to resolve advertiser delivery issues in minutes instead of days. Beyond the core agent work, you may find yourself diving into causal inference to measure recommendation effectiveness, prototyping proactive anomaly detection, or contributing to evaluation science for systems that reason over complex operational data. About the team The Demand Enablement, Product Analytics and Operations team builds the diagnostic and intelligence layer for Amazon DSP, the demand-side platform powering Amazon's programmatic advertising business. We own the systems that detect, diagnose, and surface delivery issues across campaigns, giving internal teams and advertisers the visibility to act before problems impact spend. Our product portfolio spans automated troubleshooting platforms, advertiser-facing delivery insights, and AI-powered root cause analysis using multi-agent architectures on foundation models. We are a small, high-ownership team that ships production systems end-to-end, from data pipelines processing billions of bid events to LLM-based agents that reason over complex advertising systems. If you want to work at the intersection of applied science, distributed systems observability, and real business impact measured in advertiser dollars recovered, this is the team.

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through 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. This position will be part of the Conversational Ad Experiences team within the Amazon Advertising organization. Our cross-functional team focuses on designing, developing and launching innovative ad experiences delivered to shoppers in conversational contexts. We utilize leading-edge engineering and science technologies in generative AI to help shoppers discover new products and brands through intuitive, conversational, multi-turn interfaces. We also empower advertisers to reach shoppers, using their own voice to explain and demonstrate how their products meet shoppers' needs. We collaborate with various teams across multiple Amazon organizations to push the boundary of what's possible in these fields. We are seeking a science leader for our team within the Sponsored Products & Brands organization. You'll be working with talented scientists, engineers, and product managers to innovate on behalf of our customers. An ideal candidate is able to navigate through ambiguous requirements, working with various partner teams, and has experience in generative AI, large language models (LLMs), information retrieval, and ads recommendation systems. Using a combination of generative AI and online experimentation, our scientists develop insights and optimizations that enable the monetization of Amazon properties while enhancing the experience of hundreds of millions of Amazon shoppers worldwide. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities - Serve as a tech lead for defining the science roadmap for multiple projects in the conversational ad experiences space powered by LLMs. - Build POCs, optimize and deploy models into production, run experiments, perform deep dives on experiment data to gather actionable learnings and communicate them to senior leadership - Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production. - Work closely with product managers to contribute to our mission, and proactively identify opportunities where science can help improve customer experience - Research new machine learning approaches to drive continued scientific innovation - Be a member of the Amazon-wide machine learning community, participating in internal and external meetups, hackathons and conferences - Help attract and recruit technical talent, mentor scientists and engineers in the team