Hybrid Applied Reinforcement Learning Engineer

at Thermo Fisher Scientific

• Job title: Applied Reinforcement Learning Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $150,000 - $160,000 per year
• Degree requirement: Postgraduate Degree
• Tech skills: Python, PyTorch, Tensorflow
• Location requirements: Hybrid, Palo Alto, United States

About the role

• Applied RL Engineer at Centific designing RL environments for enterprise workflows. Working at the intersection of RL research and production systems with a focus on AI agent performance.

Responsibilities

• Design and build custom RL environments (digital twins) simulating enterprise workflows: document processing, compliance, onboarding, 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

• Deep RL expertise: 3+ years hands-on experience with environment design, reward engineering, policy optimization
• LLM post-training: Experience fine-tuning LLMs using RLHF, DPO, PPO, or similar
• Production skills: Software engineering beyond research with scalable pipelines and training infrastructure
• Agentic AI: Experience with LLM-based agents, tool use, multi-step reasoning
• Technical stack: Strong Python; Gymnasium, RLlib, Stable Baselines; PyTorch/JAX/TensorFlow
• Education: MS/PhD in CS, ML, or related field (or equivalent experience)

Benefits

• Lead the frontier: Shape a new discipline at the intersection of RL, simulation, and enterprise AI
• Ship your science: See your research power real systems across healthcare, finance, and safety
• Collaborate with leaders: Work alongside NVIDIA, Microsoft, and the global AI community
• Build what matters: Create governed, compliant AI systems enterprises can trust