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Lead Research Engineer, Data Quality

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Lead Research Engineer, Data Quality in USA

  • San Francisco, CA

Responsibilities

  • Lead development of systems for evaluating tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows.
  • Define quality standards, metrics, experiments, and processes for assessing AI-agent outputs.
  • Develop scalable synthetic-data validation methods, including failure analysis, task mutation checks, and trajectory audits.
  • Partner with research engineers, domain experts, and data vendors to diagnose issues and improve data-generation workflows.
  • Build production tools, dashboards, validation pipelines, and feedback loops from research insights.
  • Mentor research engineers and promote technical rigor and clear communication.

Requirements

  • At least 5 years of research or engineering experience building AI or machine-learning data evaluation and quality systems.
  • Experience leading technical teams or projects from problem definition through implementation and iteration.
  • Advanced proficiency in Python, Docker, and Linux, with a technical education or background.
  • Strong understanding of AI evaluations, post-training, and the qualities of realistic, learnable, diverse, and reliable agent-training data.
  • Experience building evaluation infrastructure, benchmarks, synthetic-data pipelines, or validation workflows.
  • Ability to translate domain expertise and research insights into scalable review systems and production data pipelines.
  • Strong written communication and ability to work independently in an early-stage startup environment.

Benefits

  • Salary range of $150,000 to $180,000 annually.
  • Visa sponsorship is available.
  • On-site role in San Francisco, California, United States.

Salary: $150k - $180k/yr

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