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# Founding Reinforcement Learning Engineer

- **Company:** ONE STOP COLLECTIBLE CORP
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $125,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Clusters, Nvidia CUDA, Distributed Computing Environment, Python (Programming Language), Machine Learning, Open Source Technology, Reinforcement Learning, Pytorch, Large Language Models, Build Management, Kubernetes, Information Technology, Hardware Infrastructure
- **Published:** October 6, 2026
- **Apply:** https://startup.jobs/founding-reinforcement-learning-engineer-clera-10303068

## About the Role

* 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.

## Description

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.