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About the Role
Our client is hiring a Founding Reinforcement Learning Engineer to build the core RL systems from the ground up. You'll work directly with the founders, own the full loop from environment design to training to evaluation, and help shape the technical direction of the company.
Responsibilities
- Design and build RL environments, reward functions, and training pipelines.
- Train and fine-tune models with RL methods (PPO, GRPO, DPO, RLHF/RLAIF and similar).
- Build evaluation frameworks to measure model and agent performance.
- Run experiments fast, read results, and decide what to try next.
- Scale training on GPU clusters and keep pipelines reliable.
- Turn research ideas into production systems.
- Help set engineering culture and hire the next engineers.
Requirements
- 2+ years of hands-on experience in reinforcement learning or ML engineering.
- Strong Python skills and deep experience with PyTorch (or JAX).
- Hands-on work training models with RL (policy gradient methods, reward modeling, RLHF or similar).
- Experience with LLM post-training or agent training is a big plus.
- Comfortable with distributed training and GPU infrastructure.
- Builder mindset: you ship fast and are happy in a small, early team.
- Degree in CS, Math, Physics, or a related field (MS/PhD a plus).
Skills
- Python
- PyTorch
- JAX
- Reinforcement Learning
- ML Engineering
- LLM post-training
- Agent training
- Distributed training
- GPU infrastructure
- RL methods (PPO, GRPO, DPO, RLHF/RLAIF)
- Gymnasium
- Ray RLlib
- Isaac
- MuJoCo
- TRL
- verl
- OpenRLHF
Location
- San Francisco, CA
Work Type
- On-site
- Full-time
Experience Level
- 2+ years
Education Level
- Degree in CS, Math, Physics, or a related field (MS/PhD a plus)