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.

The Principal Applied Scientist will lead the development and deployment of generative AI technologies for Amazon Ads, specifically within the Sponsored Products and Brands team. This role involves reinventing advertising experiences by integrating AI into the ad lifecycle, from creation to performance analysis. The scientist will invent new product experiences, bring state-of-the-art GenAI models to production, and define the long-term science vision for the advertising business, collaborating closely with science, product, and engineering teams to deliver high-impact products.

Principal Applied Scientist for Amazon Payments AI/ML Team, focusing on AI services for payments, recommendation, prediction, and GenAI for SOP automation. The role involves setting AI direction, mentoring scientists, partnering with engineering to deliver ML/GenAI features, identifying research directions, creating roadmaps, and bringing research to production. Requires a PhD or MSc with 10+ years of experience, a track record of thought leadership, and strong collaboration skills.

Machine Learning Software Development Engineer focused on computer vision models for robotics applications within Amazon's fulfillment and delivery network. The role involves designing, building, and maintaining end-to-end ML solutions from data collection and training to deployment on edge devices, with a strong emphasis on operationalizing research models and ensuring model health in production.

Senior Manager of Applied Science at Amazon, leading a multidisciplinary team to build the next generation of personalized shopping experiences. The role involves developing state-of-the-art LLM-based techniques, deep learned transformer models for customer intent, and large-scale real-time multi-task ranking systems. The goal is to create AI primitive systems that empower other teams and directly impact millions of customers through personalized features.

Manager for a Continuous Model Evaluation and Learning workstream within Amazon Ads' Sponsored Products and Brands team. The role involves leading a team of applied scientists and engineers to build and ship an evaluation and remediation framework for an agentic brand-intelligence system. This includes designing evaluation metrics, developing optimization engines for prompts and synthetic data, and ensuring offline-to-online consistency for quality improvements. The goal is to enable autonomous detect-diagnose-remediate loops to scale quality across brand skills.

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.

Scientist role focused on designing and developing machine learning solutions for customer behavior analytics, utilizing deep learning, LLMs, recommendation systems, and reinforcement learning. Key responsibilities include fine-tuning generative models, developing recommendation and decision models, building behavioral representations, applying post-training optimization, and creating evaluation frameworks. The role emphasizes measurable business impact and customer satisfaction.

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.

The Data Scientist will focus on developing evaluation and benchmarking datasets for generative AI capabilities within the Amazon Quick Suite enterprise AI platform. This includes leveraging LLMs for synthetic data generation, creating ground truth datasets, leading human annotation initiatives, and contributing to Responsible AI efforts to ensure enterprise-readiness, safety, and effectiveness of AI at scale.

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.

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 role focuses on designing and developing machine learning solutions for customer behavior analytics at Amazon. Key responsibilities include fine-tuning language and generative models, developing recommendation and decision models, building temporal representations of customer behavior, and applying post-training optimization techniques. The role also involves developing evaluation frameworks and working with business and engineering teams to drive personalized customer experiences and business impact.

This role focuses on developing and scaling a machine learning compiler for AWS Neuron, which optimizes the performance of neural network models on custom AWS hardware accelerators (Inferentia and Trainium). The engineer will architect and implement features for the compiler stack, which integrates with popular ML frameworks, aiming to improve inference and training performance for large ML workloads.

Sr. Applied Scientist at Amazon Prime Video focused on developing and launching AI solutions for personalization and discovery systems, impacting millions of customers.

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.

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.

Director of Science, Geospatial at Amazon, leading a team of ~50 scientists focused on AI/ML solutions for last-mile delivery operations. The role involves developing and deploying solutions for geospatial problems, including address validation, place datasets, road networks, and leveraging edge data. Key focus areas include GenAI (LLMs, VLMs, agents), computer vision, and traditional ML to optimize delivery routes, improve data fidelity, and drive business impact. The role requires interfacing with senior stakeholders, strategic planning, and building a high-performing team.

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.

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.

Senior Applied Scientist role focused on developing and deploying AI/ML models and analytics for customer-facing AI solutions within Amazon Connect. The role involves working directly with customers to accelerate production deployments, designing and building AI solutions, conducting experiments, quantifying business value, and applying NLP/generative AI techniques. It spans conversational analytics and agentic AI capabilities, with a strong emphasis on driving measurable business impact and operational excellence in customer 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.

This role focuses on applying Generative AI and LLMs to transform ad experiences on Amazon's homepage and other surfaces, impacting product discovery and customer engagement. It involves building and deploying models for ad retrieval, auctions, and personalized shopping experiences, operating across the full stack from backend systems to the user-facing layer.

Machine Learning Engineer to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage.

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.

Manager for an AWS Generative AI Innovation Center focused on building and delivering generative AI solutions for customers, managing a team of scientists and engineers, and driving adoption and strategic relationships.

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 developing advanced robotics systems that integrate AI, control systems, and mechanical design for automation. The scientist will define the scientific roadmap for whole body control and dexterous manipulation, applying deep learning and LLMs to solve complex operational challenges in dynamic environments. The role involves research and practical implementation of AI in physical robotic hardware, with a focus on shipping these systems.

Senior Manager to lead an AI-first security technology organization, owning the enterprise AI/ML roadmap, leading a team of scientists and BIEs, and delivering production AI/ML models for efficiency gains and loss reduction. The role involves establishing AI/ML delivery standards, building MLOps infrastructure, and partnering with business and technical leaders, while ensuring responsible AI and compliance with regulations.

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.

Applied Scientist II role at Amazon focusing on designing and developing scalable machine learning solutions for language translation services using GenAI/LLMs. The role involves applying expertise in LLM models, conducting data analysis, and collaborating with cross-functional teams to improve translation accuracy and efficiency for millions of customers worldwide.

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.

Software Development Engineer role focused on building the platform for validating safety-critical autonomous systems. This involves designing scenario generation pipelines, integrating generative AI models for realistic behaviors, creating synthetic sensor data, and developing export connectors for simulation platforms. The role spans the full lifecycle from data curation to deployment monitoring, with a focus on automating testing and exploring edge cases.

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 to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage.

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