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Head of Computer Use/ AI Engineer
Head of Computer Use/ AI EngineerEdward Mann • Sonoma, CA, United States
Head of Computer Use / AI Engineer

Head of Computer Use / AI Engineer

Edward Mann • Sonoma, CA, United States
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  • [job_card.full_time]
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Edward Mann are hiring for an excellent Technology Start-up based in San Francisco.

About the Role

We are seeking a Head of Computer Use / Senior AI Engineer (hands on role). Driving the evolution of the next generation of browser agents (testing browser agents).

To lead the design, training, and advancement of next-generation AI agent systems. This role involves fine-tuning large language models (LLMs) and developing intelligent agents capable of navigating and interacting with real web environments. It’s a high-impact position combining hands-on engineering, experimentation, and strategic input. You’ll collaborate closely with technical leadership, contribute directly to product direction, and mentor other engineers in developing advanced agent capabilities.

Key Responsibilities

  • Develop, train, and deploy LLM-powered agents that interact with websites through real browser interfaces.
  • Fine-tune foundation models using advanced methods (e.g., LoRA, PEFT, DPO, RLHF) and select the best approach for each use case.
  • Design reinforcement learning systems that improve agent reasoning, adaptability, and task performance.
  • Own the full agent pipeline—from model architecture and policy design to simulation frameworks and testing environments.
  • Rapidly prototype, run experiments, and refine solutions to push the limits of agent performance.
  • Partner with technical leadership to shape product direction and research priorities.
  • Mentor and support other engineers, helping to grow a strong, mission-driven technical team.
  • Build and coordinate multi-agent workflows with structured roles, memory systems, and effective trajectories.

What You’ll Bring

  • Strong background in machine learning with a PhD or equivalent industry experience in AI / ML / Computer Science.
  • Hands-on experience fine-tuning LLMs.
  • Deep applied knowledge of reinforcement learning techniques.
  • Experience building agents for real-world applications (bonus : browser-based or robotics experience).
  • Strong coding and experimentation skills, with a preference for practical problem-solving over theory alone.
  • A sense of ownership and drive to build impactful systems beyond titles or hierarchy.
  • Experience mentoring, managing, or growing technical teams.
  • Preferred Qualifications

  • Record of impactful research publications or open-source contributions.
  • Experience in high-growth, fast-paced start-up environments.
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