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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 28, 2026. First seen by Alion on Oct 27, 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 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

  • 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 what to explore next.

  • Scale training on GPU clusters and keep training pipelines reliable.

  • Turn research ideas into production systems.

  • Help establish engineering practices and hire future team members.

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 model or agent evaluation.

  • A track record of taking research ideas into production systems.

  • A degree in computer science, mathematics, physics, or a related field; a master's or PhD is a plus.

  • 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.

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