Sr. Applied Scientist - Agentic AI, Amazon Fulfillment Technology

Amazon • Bellevue, WA

1d

About The Position

As a Senior 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.

About the team
Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment
network. We invent and deliver software, hardware, and data science solutions
that orchestrate processes, robots, machines, and people. We harmonize the
physical and virtual world so Amazon customers can get what they want, when they
want it. Learn more about AFT: https://tinyurl.com/AFTOverview

Requirements

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Nice To Haves

• Experience in building machine learning models for business application

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
• 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.

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree