Staff Research Scientist – Reinforcement Learning

Job not on LinkedIn

🕒 July 3

🏄 California – Remote

🏄 California – Remote

💵 $200k - $250k / year

⏰ Full Time

🔴 Lead

🧬 Research Scientist

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Thermo Fisher Scientific

WebsiteLinkedInAll Job Openings

10,000+ employees

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

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 • 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 • Working knowledge of modern post-training and rollout-serving libraries (TRL, veRL, OpenRLHF, SkyRL)

🏖️ Benefits

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Remote work options