Founding Reinforcement Learning Engineer

Build core reinforcement learning systems from the ground up as a founding engineer on an early-stage AI team. You will work directly with the founders and own the process from environment design and model training through evaluation, helping shape the company’s technical direction., * 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 which approaches to explore next.
• Scale training on GPU clusters and maintain reliable pipelines.
• Turn research ideas into production systems.
• Help establish engineering culture and hire future engineers.

• At least 2 years of hands-on experience in reinforcement learning or machine learning engineering, with relevant experience potentially ranging from 2 to 10 or more years.
• Strong Python skills and deep experience with PyTorch or JAX.
• Practical experience training models with reinforcement learning, including policy gradient methods, reward modeling, or RLHF.
• Comfort with distributed training and GPU infrastructure, including tools such as Ray, CUDA, and Kubernetes.
• Experience with LLM post-training or agent training is a plus, as is familiarity with Gymnasium, Ray RLlib, Isaac, MuJoCo, TRL, verl, or OpenRLHF.
• A degree in computer science, mathematics, physics, or a related field is sought; a master’s or PhD is a plus. Publications or open-source work in RL are also valued.
• A hands-on builder who enjoys moving quickly in a small, early-stage team.