2 days ago
Base Salary
$125k - $200k/yr
Responsibilities
- Design and build reinforcement learning environments, reward functions, and training pipelines.
- Train and fine-tune models using PPO, GRPO, DPO, RLHF, and RLAIF.
- Build evaluation frameworks for measuring model and agent performance.
- Run experiments, analyze results, and guide research priorities.
- Scale training across GPU clusters and maintain reliable pipelines.
- Bring research ideas into production systems and establish engineering practices.
- Contribute to hiring as the engineering team grows.
Requirements
- At least 2 years of hands-on experience in reinforcement learning or machine learning engineering.
- Strong Python skills and deep experience with PyTorch or JAX.
- Practical experience with policy gradient methods, reward modeling, RLHF, or similar techniques.
- Experience with distributed training, GPU infrastructure, RL environments, reward functions, training pipelines, and model or agent evaluation.
- A track record of turning research ideas into production systems.
- A degree in computer science, mathematics, physics, or a related field.
- Experience with LLM post-training, agent training, simulation frameworks, or RL training libraries is a plus.
- Graduate study is a plus.
Benefits
- Visa sponsorship is available.
- This is an on-site role in San Francisco, California.