Staff Research Scientist – Reinforcement Learning

Job not on LinkedIn

🔥 2 hours ago

🏄 California – Remote

🏄 California – Remote

💵 $200k - $250k / year

⏰ Full Time

🔴 Lead

🧬 Research Scientist

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Thermo Fisher Scientific

WebsiteLinkedInAll Job Openings

10,000+ employees

⚕️ Healthcare Insurance

🧬 Biotechnology

💊 Pharmaceuticals

Healthcare Insurance • Biotechnology • Pharmaceuticals

Thermo Fisher Scientific is a leading global supplier of scientific instrumentation, reagents and consumables, and software services. They support the life sciences, healthcare, and analytical chemistry sectors by providing robust solutions for laboratory research and production processes. Their innovative products and services encompass a range of applications, including diagnostics, lab workflow automation, and drug discovery.

📋 Description

• Design simulation environments and digital twins for enterprise workflows • Post-train LLM agents using RLHF, DPO, GRPO, PPO, and emerging methods • Build pipelines that convert human-labeled traces and verifiable signals into training data • Architect multi-turn, tool-using agents with closed learning loops • Design reward functions and verifiers that resist reward hacking and reflect real task outcomes • Set the technical bar across the team — architecture, code review, engineering standards • Mentor researchers and engineers; drive technical direction through influence • Translate research into production; contribute to publications

🎯 Requirements

• 7+ years in ML/AI research or engineering; 3+ years at senior/staff level • MS or PhD in Computer Science, Machine Learning, or related field (or equivalent) • 5+ years hands-on RL — environment design, reward engineering, policy optimization — with at least one production deployment LLM Post-Training • 3+ years fine-tuning LLMs with hands-on RL post-training (RLHF, DPO, GRPO, PPO) • Expert-level implementation of RLHF pipelines, reward modeling (Bradley-Terry), DPO, and KTO • Working knowledge of modern post-training and rollout-serving libraries (TRL, veRL, OpenRLHF, SkyRL) • Experience building LLM-based agents: tool use, multi-turn reasoning, trajectory evaluation • Strong Python and software engineering skills — comfortable building production pipelines, not just notebooks • Deep expertise in MDPs, policy gradient methods (PPO, SAC), and temporal difference learning • Hands-on experience with Gymnasium-based environments and reward engineering (sparse vs. dense)

🏖️ Benefits

• N/A