Note: The job is a remote job and is open to candidates in USA. Centific is a frontier AI data foundry that empowers clients with safe, scalable AI deployment. The Applied Reinforcement Learning Engineer will design and build RL environments to simulate complex enterprise workflows and train intelligent agents, bridging research and production systems.
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
Skills
- 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)
- Publications at NeurIPS, ICML, ICLR, ACL, or similar venues
- Enterprise workflow experience in healthcare, finance, logistics, or compliance
- Open-source contributions to CleanRL, TRL, veRL, or agent frameworks
- Experience with world models, synthetic data generation, and simulation
- Distributed training and large-scale RL experimentation
Company Overview
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