About this role
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
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Design and build reinforcement learning environments, reward functions, and training pipelines.
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Train and fine-tune models using methods such as PPO, GRPO, DPO, RLHF, and RLAIF.
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Develop evaluation frameworks to measure model and agent performance.
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Run experiments, interpret results, and decide what to explore next.
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Scale training on GPU clusters and keep training pipelines reliable.
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Turn research ideas into production systems.
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Help establish engineering practices and hire future team members.
What We're Looking For
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At least 2 years of hands-on experience in reinforcement learning or machine learning engineering.
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Strong Python skills and deep experience with PyTorch or JAX.
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Practical experience training models with reinforcement learning methods, including policy gradients, reward modeling, or RLHF.
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Experience with distributed training, GPU infrastructure, RL environments, reward functions, training pipelines, and model or agent evaluation.
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A track record of taking research ideas into production systems.
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A degree in computer science, mathematics, physics, or a related field; a master's or PhD is a plus.
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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.