We are building foundational LLMs for Amazon Stores that fuse world knowledge with deep e-commerce understanding to power next-generation shopping experiences. These systems continuously learn from real-world customer interactions to become more helpful, personalized, and context-aware over time.
We are looking for builders who are passionate about large-scale systems, AI innovation, and customer impact. You will work at the intersection of distributed systems, machine learning infrastructure, and science to bring frontier research—especially in post-training and reinforcement learning—into production at Amazon scale.
Key job responsibilities
* Architect and build scalable ML infrastructure powering LLM training and post-training workflows, including supervised fine-tuning, reinforcement learning, and continuous learning from live traffic
* Transform real-world customer interactions into high-quality training signals, enabling continuous model improvement and better customer experiences
* Build and optimize post-training and RL systems, including reward modeling, policy optimization, data collection loops.
* Drive experimentation and iteration velocity by building tooling and frameworks that enable rapid hypothesis testing, signal validation, and model quality improvements
* Partner closely with applied scientists to translate frontier techniques (e.g., RLHF, agentic workflows, multi-turn optimization) into reliable, production-grade systems
* Own systems end-to-end, including design, implementation, deployment, observability, and operational excellence
* Raise the engineering bar through technical leadership, design reviews, and mentorship, influencing best practices across the organization
