Research Intern – Applied Reinforcement Learning

Job not on LinkedIn

🕒 July 3

🏄 California, Washington – Remote

🏄 California – Remote ☕ Washington – Remote

💵 $35 - $45 / hour

👨‍🎓 Internship

⚪️ Entry-level

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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 evaluate reinforcement learning (RL) systems for agentic AI workflows • Develop RL environments, reward models, and post-training pipelines for LLM-based agents • Create end-to-end RL pipelines for agentic systems (simulation → training → evaluation) • Align LLM-based agents using RLHF, DPO, PPO, and emerging methods • Design reward functions, verifiers, and evaluation frameworks • Build simulation environments (digital twins) for enterprise workflows • Ensure scalable training and inference for RL-based systems • Document experiments, ablations, and findings for research and productionization

🎯 Requirements

• PhD candidate in CS, ML, or related field with research in reinforcement learning or agentic AI • Strong Python and PyTorch skills with GPU-based training experience • Solid understanding of RL fundamentals (MDPs, policy gradients, value methods) • Experience with LLMs and post-training techniques (RLHF, DPO, PPO, etc.) • Strong experimentation practices (ablation, reproducibility, clear reporting) • Experience with RL environments (Gymnasium, RLlib, Stable Baselines) (preferred) • Research in offline RL, model-based RL, or hierarchical RL (preferred) • Publications at top ML conferences (NeurIPS, ICML, ICLR, ACL) (preferred) • Experience with simulation, synthetic data, or multi-agent systems (preferred) • Distributed training and large-scale experimentation (preferred)

🏖️ Benefits

• Competitive stipend • Mentorship from researchers and engineers • Access to modern GPU infrastructure • Opportunities to publish and present research