As an Applied Scientist in Amazon Fullfilment Technology, you will lead the development of agentic systems to assist with operational decision making and orchestration. You will work building full agentic systems leveraging multi-agent orchestration, tool use, memory, and action execution. You will train LLMs using a combination of rejection sampling approaches, SFT, continual post-training, and Reinforcement Learning (RL). These systems are deployed to Amazon buildings, and you will also work on rigorous offline and online evaluations. Your work will leverage the latest LLMs to develop capabilities for agentic reasoning, coding and analytics. You will also lead research projects to tackle unsolved problems, mentor interns, and author academic papers to summarize your findings for external publication.

Generating training and preference data for specific use cases (reasoning trajectories, tool traces)

Reward modeling and policy optimization for LLMs: DPO, IPO, RLHF/RLAIF with PPO/GRPO, rejection sampling.

Supervised fine-tuning on step-by-step trajectories and tool-use traces

Verbal Reinforcement Learning and Continual Learning

RL for LLMs, Offline RL and off-policy evaluation

Agentic memory/state management; episodic and semantic memory; vector search; grounding with RAG.

Evaluation: developing decision quality metrics, scaling LLM-based evaluations.