Founding Reinforcement Learning Engineer

ABOUT THE ROLE 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. WHAT YOU'LL DO - 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. WHAT WE'RE LOOKING FOR - 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. COMPENSATION & BENEFITS Base salary: $125,000 to $200,000 USD annually. Visa sponsorship is available. LOCATION On-site in San Francisco, California, United States. Candidates must be based in or willing to relocate to San Francisco.