Research Intern – Applied Reinforcement Learning
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Job Description
PhD Research Intern designing and evaluating reinforcement learning systems for agentic AI workflows at Centific. Involves developing RL environments and post-training pipelines for practical enterprise solutions.
Responsibilities:
- 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
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