Applied Scientist - Agentic AI, Amazon Fulfillment Technology

Amazon • Bellevue, WA

1d

About The Position

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.

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 models for business application experience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Nice To Haves

• Experience using Unix/Linux
• Experience in professional software development

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.

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