Join an early-stage AI team building reinforcement learning systems from the ground up. You will own the end-to-end process, from environment design and model training to evaluation, helping shape the technical direction and engineering culture.
Design and build reinforcement learning environments, reward functions, and training pipelines.
Train and fine-tune models using methods such as PPO, GRPO, DPO, RLHF, and RLAIF.
Develop evaluation frameworks to measure model and agent performance.
Run experiments, interpret results, and decide what to explore next.
Scale training on GPU clusters and keep training pipelines reliable.
Turn research ideas into production systems.
Help establish engineering practices and hire future team members.
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, including policy gradients, reward modeling, or RLHF.
Experience with distributed training, GPU infrastructure, RL environments, reward functions, training pipelines, and model or agent evaluation.
A track record of taking research ideas into production systems.
A degree in computer science, mathematics, physics, or a related field; a master's or PhD is a plus.
LLM post-training or agent training experience is beneficial, as are RL publications or open-source work and experience with simulation frameworks or training libraries.
Base salary: $125,000 to $200,000 USD annually. Visa sponsorship is available.
On-site in San Francisco, California, United States. Candidates must be based in or willing to relocate to San Francisco.