Senior Applied Reinforcement Learning Engineer

🕒 June 12

🏄 California, Washington – Remote

🏄 California – Remote ☕ Washington – Remote

💵 $150k - $300k / year

⏰ Full Time

🟠 Senior

👷🏻‍♀️ Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 45%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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 and build custom RL environments (digital twins) simulating enterprise workflows such as document processing, compliance, onboarding, and support automation • Post-train LLM-based agents on domain-specific tasks using PPO, GRPO, DPO, and RLHF • Build end-to-end pipelines converting human-labeled traces into RL training data • Architect multi-step reasoning agents with tool-calling and closed learning loops • Design reward functions, verifiers, and validation frameworks for pre-deployment testing • Translate cutting-edge RL research into production systems • Contribute to publications

🎯 Requirements

• 3+ years hands-on experience with environment design, reward engineering, and policy optimization • Experience fine-tuning LLMs using RLHF, DPO, PPO, or similar • Software engineering experience beyond research with scalable pipelines and training infrastructure • Experience with LLM-based agents, tool use, and multi-step reasoning • Strong Python skills • Experience with Gymnasium, RLlib, and Stable Baselines • Experience with PyTorch, JAX, or TensorFlow • MS/PhD in CS, ML, or related field (or equivalent experience) • Publications at NeurIPS, ICML, ICLR, ACL, or similar venues preferred • Enterprise workflow experience in healthcare, finance, logistics, or compliance preferred • Open-source contributions to CleanRL, TRL, veRL, or agent frameworks preferred • Experience with world models, synthetic data generation, and simulation preferred • Distributed training and large-scale RL experimentation preferred

🏖️ Benefits

• Opportunity to lead a new discipline at the intersection of RL, simulation, and enterprise AI • Opportunity to see research power real systems across healthcare, finance, and safety • Collaboration with NVIDIA, Microsoft, and the global AI community • Opportunity to create governed, compliant AI systems enterprises can trust