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Salary
$125k – $200k per year
Location
In office (San Francisco)
Seniority
Junior · 2+ years exp
Visa
Sponsorship offered in the posting
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 25, 2026. First seen by Alion on Oct 19, 2026. Clera scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Clera is a San Francisco company that runs an AI recruiting platform positioned as a talent agent rather than a job board, matching engineers and other technical candidates to roles at venture-backed startups. Its system reads a candidate's background and preferences, then represents them to hiring teams at companies funded by firms such as Andreessen Horowitz, Y Combinator, Index Ventures and General Catalyst, compressing the introduction step that traditional headhunting handles manually. The model is aimed at the segment where recruiter fees are highest and candidate supply is thinnest, and the platform handles screening, scheduling and pipeline tracking for both sides of the match.
Backed by TU

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 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 evaluation frameworks.

  • A track record of bringing research ideas into production, plus a degree in computer science, mathematics, physics, or a related field.

  • 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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