Agentic AI Scientist for Fulfillment Tech

Amazon Inc.; Bellevue (WA); Vor Ort; USD 143.000 - 193.000; Vollzeit

Benefits dieser Stelle

RSUs

Health insurance

401(k) matching

Paid time off

Parental leave

Zusammenfassung

Amazon Fulfillment Technology in Bellevue, WA seeks an Applied Scientist to lead development of agentic AI systems for operational decision making. You will build multi-agent orchestration, tool use, memory, and action execution, training LLMs with SFT, RL, and rejection sampling.

Expect research mentoring, intern supervision, and external publications. You will contribute to agentic memory, evaluations, and scaling of LLM-based decision quality across Amazon buildings, with a strong emphasis on

Qualifikationen

• 3+ years of building models for business applications.
• PhD, or Master's plus 4+ years in CS, CE, ML.
• Experience programming in Java, C++, Python.
• Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing.

Aufgaben

• 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.

Kenntnisse

Java

C++

Python

Algorithms & data structures

Numerical optimization

Data mining

Distributed computing

High-performance computing

Memory management

Ausbildung

PhD in CS/CE/ML

Master's + 4+ years in CS/CE/ML

Tools

Unix/Linux

Jobbeschreibung

Amazon Fulfillment Technology in Bellevue, WA seeks an Applied Scientist to lead development of agentic AI systems for operational decision making. You will build multi-agent orchestration, tool use, memory, and action execution, training LLMs with SFT, RL, and rejection sampling.

Expect research mentoring, intern supervision, and external publications. You will contribute to agentic memory, evaluations, and scaling of LLM-based decision quality across Amazon buildings, with a strong emphasis on