Founding Reinforcement Learning (RL) Engineer
•Today
| Verified Pay check_circle | Provided by the employer$125000 - $190000 per year |
|---|---|
| Hours | Full-time |
| Location | San Francisco, California |
About this job
Job Description
Job Description
Requirements
Founding RL Engineer
San Francisco, CA, on-site · Full-time
The build: 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.
What you'll do
- 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
What we're looking for
- 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).
Nice to have
- Publications or open-source work in RL.
- Experience at an AI lab or an early-stage AI startup.
- Experience with simulation or environment frameworks (Gymnasium, Ray RLlib, Isaac, MuJoCo).
- Experience with training libraries like TRL, verl, or OpenRLHF.
Logistics: San Francisco, CA, on-site.
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