Agentic AI Scientist — LLMs & Multi-Agent Orchestration

Amazon; Factoria (WA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Fulfillment Technology AI Science is seeking an Applied Scientist to lead the development of agentic AI systems that assist operational decision making and orchestration across Amazon buildings. You will train LLMs, leverage multi-agent orchestration, tool use, memory and action execution, and publish findings.

You will mentor interns and contribute to academic papers. The role emphasizes research leadership, model training, evaluation design, and collaboration with cross-functional teams

Qualifications

• PhD or Master's degree with 4+ years in CS/CE/ML.
• 3+ years building models for business applications.
• Proficiency in Java, C++, Python.
• Experience in algorithms, data structures, numerical optimization, data mining, parallel/distributed computing, HPC.

Responsibilities

• 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.

Skills

Java

C++

Python

Algorithms and data structures

Numerical optimization

Data mining

Parallel and distributed computing

High-performance computing

Education

PhD or Master's in CS/CE/ML

Tools

Unix/Linux

Job description

Amazon Fulfillment Technology AI Science is seeking an Applied Scientist to lead the development of agentic AI systems that assist operational decision making and orchestration across Amazon buildings. You will train LLMs, leverage multi-agent orchestration, tool use, memory and action execution, and publish findings.

You will mentor interns and contribute to academic papers. The role emphasizes research leadership, model training, evaluation design, and collaboration with cross-functional teams