Overview
About the Role Join an early-stage AI team building reinforcement learning systems to help AI agents handle complex work. You will own the full loop from environment design through training and evaluation, while helping shape the team's technical direction. 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. Build evaluation frameworks to measure model and agent performance. Run experiments, interpret results, and use findings to guide what to try next. Scale training on GPU clusters and maintain reliable training pipelines. Turn research ideas into production systems and help establish engineering practices. Contribute to hiring as the engineering team grows. What We're Looking For At least 2 years of hands-on experience in reinforcement learning o
Full job description
Full Job Description
About the Role
Join an early-stage AI team building reinforcement learning systems to help AI agents handle complex work. You will own the full loop from environment design through training and evaluation, while helping shape the team's technical direction.
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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Build evaluation frameworks to measure model and agent performance.
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Run experiments, interpret results, and use findings to guide what to try next.
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Scale training on GPU clusters and maintain reliable training pipelines.
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Turn research ideas into production systems and help establish engineering practices.
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Contribute to hiring as the engineering team grows.
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 evaluation frameworks.
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A track record of bringing research ideas into production, plus a degree in computer science, mathematics, physics, or a related field.
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Experience with LLM post-training, agent training, simulation frameworks, or training libraries is a plus.
Compensation & Benefits
Compensation is $125,000 to $200,000 USD annually. Visa sponsorship is available.
Location
This is an on-site role based in San Francisco, California, United States.
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