4 hours 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, interpret results, and guide future work based on findings.
- Scale training on GPU clusters and maintain reliable training pipelines.
- Turn research ideas into production systems and help 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 training models with reinforcement learning methods such as policy gradients, reward modeling, or RLHF.
- Experience with distributed training, GPU infrastructure, reinforcement learning environments, reward functions, training pipelines, and evaluation frameworks.
- A track record of bringing research ideas into production and a degree in computer science, mathematics, physics, or a related field.
- Experience with LLM post-training, agent training, simulation frameworks, or training libraries is preferred.
Benefits
- Annual compensation of $125,000 to $200,000 USD.
- Visa sponsorship is available.
- On-site role based in San Francisco, California, United States.