Senior Software Development Engineer on the Applied AI team, focusing on Knowledge Work Automation. The role involves designing, building, and shipping multi-agentic systems and AI agents for internal Amazon workflows, leveraging LLMs and production-grade distributed software. Key responsibilities include architecting agentic AI systems, innovating with LLM techniques, shipping reusable primitives, and driving end-to-end delivery.

Senior Applied Scientist to lead the development of AI models for satellite manufacturing, transforming data into an intelligence system that improves how satellites are built. Focuses on AI-native workflows like non-conformance disposition, root-cause analysis, and predictive test optimization, influencing real-world manufacturing decisions.

Senior Software Engineer to build foundational systems and consumer-facing features for trustworthy AI experiences at scale. Focus on privacy-preserving AI, responsible AI frameworks, and accessibility. Key challenges include latency vs. privacy trade-offs, AI safety, ambient computing privacy, multimodal AI systems, and real-time evaluation.

Software Development Engineer II to design and build AI-powered advertiser controls, including bidding systems, agentic architectures, and experimentation systems for Amazon's Sponsored Products and Brands. The role involves owning platform capabilities, developing AI engineering infrastructure, interfacing agentic architectures, and designing experimentation systems. Requires strong software engineering skills, experience with Gen AI/LLMs, fine-tuning, RLHF, and RAG, with a focus on delivering customer-facing AI products.

Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. The role involves designing, developing, and evaluating deep learning models for NLP and recommendation systems, building ML pipelines, running A/B experiments, and deploying models to production. The team is greenfield, aiming for direct business impact and encouraging top-tier publications alongside production deployment.

Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. Responsibilities include designing, developing, and evaluating ML models for NLP, recommendation systems, and personalization, building ML pipelines, running A/B experiments, and collaborating on production deployment.

Senior Data Scientist role focused on conversational AI, LLMs, NLP, and Generative AI for Alexa. The role involves defining strategy, leading initiatives from problem formulation to production, establishing evaluation frameworks, and driving consensus on agentic systems. It requires expertise in machine learning, generative AI, and computer vision, with a focus on delivering scalable and impactful solutions for millions of customers.

Applied Scientist II on Amazon's Trustworthy Shopping Experience (TSE) team, focusing on building and productionizing generative AI solutions for automating complex manual investigation processes at scale. The role involves designing and building agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, leveraging techniques like SFT, RFT, and few-shot approaches. The scientist will also work on prompt optimization, novel Finetuned transformer architectures, and identifying business problems to apply state-of-the-art LLM workflows. The role offers end-to-end ownership from research to production deployment, with a focus on impacting cost-of-serving customers while maintaining trust and safety.

Manager for a team building an agentic GenAI platform for AWS data center operations, focusing on LLM orchestration, agent frameworks, search/knowledge systems, and full-stack serverless engineering.

Applied Scientist II role focused on building AI-driven solutions for Amazon Travel & Events, leveraging Generative AI, LLMs, NLU, conversational AI, and Applied ML. Responsibilities include designing, developing, and evaluating ML models using GenAI, multimodal reasoning, and information retrieval for catalog understanding, applying VLMs and LLM-based approaches with fine-tuning and RAG, implementing model optimization techniques for efficiency, driving experiments, building ML pipelines, contributing to model reliability through interpretability and calibration, and collaborating with teams to translate business requirements into ML solutions. The role also involves staying current with research and co-authoring publications.

Senior Applied Scientist role focused on building scalable neuro-symbolic systems that fuse formal reasoning with GenAI and agentic AI for AWS customers, aiming to deliver reliable, verifiable outcomes and enhance features like hallucination detection and guardrails. The role involves end-to-end ownership from research to production, collaboration, and mentoring.

The role focuses on building a personalized and context-aware agentic advertiser guidance system using LLMs and sophisticated tooling for Amazon Ads. It involves integrating LLMs with tools, operating across various advertiser-facing platforms, and delivering solutions through advanced agent architectures and model customization.

Software Development Engineer role focused on building an Agent platform using Generative AI and ML technologies for Amazon Ads. The role involves designing and developing scalable systems for personalized ad experiences, optimizing for cost and latency, and pioneering new approaches to conversational AI. The team owns the SPB Agent, which powers reasoning behind agentic experiences in Ads Console, Sales, and Seller Central.

Software Development Engineer role focused on building AI agents and tools to simplify and accelerate customer adoption of Amazon's AWS Neuron ML software stack, which supports Trainium and Inferentia ML chips. The role involves applying Generative AI to AI itself, identifying obstacles, and developing solutions to improve the porting and optimization of ML workloads on AWS ML silicon.

Data Scientist II role focused on building and productionizing personalized Gen AI systems for Amazon's global selling partners, with a focus on Agentic, RL, and forecasting products. Requires expertise in ML/DL frameworks, agentic frameworks, and experience with complex AI systems and data pipelines.

Data Scientist II role focused on building AI-first security products, including designing, training, and shipping ML models for agentic systems, anomaly detection, and threat classification. The role involves owning the full ML lifecycle, using AI tools to accelerate development, and powering multi-agent security systems with RAG pipelines and evaluation frameworks. It emphasizes rapid prototyping, customer validation, and collaboration across disciplines.

Senior Security Engineer focused on offensive security operations and research for AI systems, including training pipelines, inference systems, and model architectures. The role involves discovering and exploiting vulnerabilities, developing automation for threat emulation, and collaborating with engineering teams to improve AI security posture.

Data Scientist II role focused on building AI-first security products, including agentic systems, anomaly detection, and threat classification. The role involves the full ML lifecycle, from problem framing to production deployment and monitoring, with an emphasis on using AI tools to accelerate development. Key responsibilities include powering agentic architectures with models, embeddings, RAG pipelines, and evaluation frameworks, rapid prototyping, and customer validation. The role also involves partnering across disciplines and communicating complex results. The team operates with startup speed at Amazon scale, emphasizing rapid iteration and shipping.

Senior Software Engineer role focused on building AI systems for satellite manufacturing intelligence. The role involves architecting and implementing a platform that connects design, production, test, and quality data, utilizing AI-native workflows with retrieval systems, foundation models, agentic tool use, and human review. Key responsibilities include designing AI-native workflows, creating evaluation mechanisms for AI quality, and building production software. The role emphasizes building agentic systems (L4) with a strong focus on evaluation and quality gates (L5).

AI Specialist Solutions Architect for AWS, focusing on helping enterprise customers adopt and scale GenAI, ML, and Agentic technologies. The role involves designing technical architectures, advising on best practices, and acting as a trusted advisor to customers, with a strong emphasis on production deployment and operational efficiency.

Senior Applied Scientist role focused on building AI-powered tooling for AWS Security operations, including generative AI incident response assistants, natural language-driven response, detection enrichment pipelines, and security data analytics platforms. The role involves defining and executing the ML/AI roadmap, extending and inventing techniques at the product level, and bringing models from research into production systems. Responsibilities include LLM-powered incident triage, anomaly detection, RAG, prompt engineering, fine-tuning, developing evaluation frameworks, and mentoring engineers.

Applied Scientist II role focused on building and deploying machine learning models and agentic AI systems for payment risk management and fraud detection at Amazon. The role involves end-to-end development, from data analysis and model design to production deployment and monitoring, utilizing techniques like deep learning, LLMs, graph neural networks, and multi-agent systems.

The Applied Scientist II role on the Brand Registry team at Amazon focuses on designing, developing, and deploying AI solutions, specifically leveraging LLMs and agentic AI frameworks to create intelligent automation and autonomous outcomes for brand protection and seller experience. The role involves owning the end-to-end ML lifecycle, from problem formulation to production deployment, and collaborating with product managers and engineering teams.

This role focuses on leading AI-focused customer engagements end-to-end, partnering with engineers and designers to deliver AI solutions using technologies like LLMs, RAG, and autonomous agents. The goal is to help customers experiment with innovative approaches and validate the technical feasibility of AI solutions, shaping how organizations adopt AI.

Applied Scientist at AWS Marketplace focused on building and improving AI/ML-powered discovery systems. The role involves developing models for search ranking, query understanding, and recommendations, and extending these into agentic discovery experiences using multi-agent systems. Collaboration with engineers and product managers to deploy solutions into production is key.

Software Development Engineer II role on the Same Day Delivery Experience team, focusing on building and scaling AI-powered tools using LLMs, RAG, NLP, prompt engineering, and agentic AI workflows to enhance customer experience and protection. Responsibilities include designing and building AI tools, developing retrieval pipelines, prototyping agentic AI capabilities, and working on scalable AI/ML systems.

This Principal Software Engineer role focuses on building agentic AI solutions for AWS DevOps, aiming to accelerate incident response and improve operational efficiency for production systems. The role involves working with information retrieval systems, knowledge graphs, and LLMs to create a frontier agent that resolves incidents and learns from them for systemic improvements.

This role focuses on managing AI-focused customer engagements end-to-end, partnering with engineers and designers to deliver AI solutions using technologies like LLMs, RAG, and autonomous agents. The role involves orchestrating customer engagements, facilitating solution design, identifying opportunities, building relationships, and ensuring responsible AI practices.

The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research.

The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research.

Sr. Applied AI Solutions Architect focused on accelerating customer adoption of Amazon Connect's AI capabilities. The role involves guiding customers in model selection (via Amazon Bedrock), prompt configuration for AI agents, and architecting tool integrations (APIs, Lambda, etc.) for agentic AI systems. A key aspect is ensuring customer data readiness for AI agents and RAG. The role is hands-on, requiring coding, building integrations, and configuring agents, working at the intersection of contact center operations and applied AI.

Senior Software Development Engineer to lead AI/ML/LLM/VLM/VLA development for automating manufacturing engineering workflows, including agentic AI, robotic control, and computer vision for quality assurance.

Research and develop generative AI technology for Amazon Connect, focusing on LLM Agents and their evaluation/optimization to disrupt customer service experiences. The role involves building ML models from conception to deployment, prototyping, and iterating on state-of-the-art Agentic AI systems.

This role manages a team of applied scientists and engineers focused on developing ML and RL algorithms for robotic systems to optimize stow strategy and warehouse capacity. It involves leading research, design, deployment, and evaluation of these systems, with a focus on transformer architectures, affordance learning, and geometric reasoning in high-density environments.

Manager for an Applied Science team focused on LLM-powered conversational AI for Alexa, encompassing agent execution, understanding, reasoning, evaluation, and runtime systems. The role involves leading scientists, developing platforms, driving innovation, and collaborating across functions to deliver scalable production solutions and advance research.

Seeking an Applied Scientist with expertise in AI, Agentic LLMs, Generative AI, Machine Learning, and NLP to build LLM-powered solutions for Amazon's BuyForMe product. The role involves developing agentic frameworks, LLM fine-tuning, reinforcement learning, prompt engineering, RAG, MCP, and automated benchmarking to improve shopping workflows.

Senior Applied AI Solutions Architect for Federal Financial Regulatory customers, focusing on designing and enabling AI/ML solutions for fraud detection, market surveillance, regulatory reporting, and consumer protection. The role involves technical guidance, developing reference architectures, and enabling customer adoption of AI/ML on AWS, with a strong emphasis on agentic systems and RAG.

Applied Scientist II role at Amazon Business focused on revolutionizing sales productivity using AI-powered solutions. The role involves developing tools for Account Executives (AEs) to prioritize accounts, recommend products, and engage customers more effectively. It leverages machine learning and Generative AI to outreach customers based on their behavior and purchase history, and performs text mining on customer conversations to recommend solutions. The scientist will partner with product, tech, and sales teams to launch and scale global AI products, with a focus on improving customer experience and sales efficiency.

This role involves designing, implementing, and fine-tuning state-of-the-art Generative AI solutions for AWS customers, focusing on real-world problem-solving and production deployment. The architect will collaborate with customers and internal teams to understand business needs, develop proof-of-concepts, and guide adoption patterns.

Manager for the Amazon Sponsored Agent (ASA) team, focusing on building and scaling a new agentic service for conversational and agentic ads. The role involves leading a team to develop a multi-agent system architecture for contextual ad serving, conversation understanding, and commercial insights generation, with a focus on AI-native ad formats.

This role leads the science function in WW Stores Finance, driving AI/ML innovations in financial analytics. The leader builds and directs a multidisciplinary team to deliver scalable solutions, translating AI capabilities into production systems. The role requires strategic vision and execution excellence to transform finance operations, automate workflows, and improve forecasting and controllership through agentic AI, ML, and generative AI.

Senior Applied Scientist role at Amazon's FinTech organization, focusing on building and scaling generative AI applications and autonomous agents for financial operations. The role involves developing systems that process financial transactions, extract intelligence from documents, and power agents that learn from customer interactions. Key responsibilities include ensuring AI systems are trusted for compliance, designing agents that improve with user feedback, optimizing inference at scale using tiered models and LLMs, and developing robust evaluation frameworks. The position emphasizes shipping production-ready models, working across the full stack, and solving complex real-world financial problems.

This role focuses on designing and implementing innovative AI solutions, developing ML models and frameworks, enabling self-service automation, and building evaluation frameworks to enhance productivity and unlock new value within Audible. The role involves applying ML/AI approaches to solve complex real-world problems and building the blueprint for how Audible works with AI.

Sr. Prototyping Architect for AWS PACE team, building functional Generative AI and Agentic AI prototypes with customers using AWS AI services. Focus on architecting, developing, and guiding customers through complex technical decisions on LLMs, agent design patterns, and AI adoption strategies, with a path to production.

Senior Applied Scientist role focused on improving advertising performance and delivering innovative advertising experiences for Amazon devices and grocery. The role involves building and deploying machine learning models, with a specific emphasis on agentic AI for ads targeting, including autonomous agents, multi-agent orchestration, large multimodal models, reinforcement learning, and sequential decision making. The position requires experience in developing scalable data pipelines, optimizing conversion KPIs, and staying updated with the latest advancements in ML, NLP, and multimodal learning.

Machine Learning Engineer role focused on designing, implementing, and scaling AI/ML solutions for AWS customers. This involves selecting, fine-tuning, and deploying models, identifying use cases, and providing technical guidance on responsible AI adoption. The role requires experience with ML/statistical modeling, software engineering best practices, and a Top Secret security clearance.

Machine Learning Engineer on AWS Professional Services team, focusing on designing, implementing, and scaling Generative AI solutions for customers. Requires TS/SCI clearance.

Applied Scientist II role focused on detecting sophisticated invalid traffic (IVT) in advertising using deep learning, self-supervised techniques, representation learning, and advanced clustering. The role involves defining research problems, inventing ML approaches, designing and deploying production-quality ML components, and working with massive datasets. It also requires producing research reports and contributing to the scientific community through publications.