Senior Applied Reinforcement Learning Engineer

🕒 June 12

🏄 California, Washington – Remote

🏄 California – Remote ☕ Washington – Remote

💵 $150k - $300k / year

⏰ Full Time

🟠 Senior

👷🏻‍♀️ Engineer

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👻 Ghost score 45%

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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

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