Lead Research Engineer, Data Quality (Visa Sponsorship - United States)
This is a leadership role at an early-stage AI company that builds training data for AI agents. You will lead a research engineering group focused on measuring and improving the quality of data used to train and evaluate agents, including reinforcement learning environments, synthetic datasets, benchmarks and domain-specific workflows. You will set the quality strategy and the systems behind it, and work closely with researchers, domain experts and data vendors.
What you'll do
- Lead a team that builds systems to evaluate large volumes of tasks across environments, datasets, benchmarks and specialized workflows.
- Define data quality strategy by building quality-control systems, setting standards and designing experiments to assess agent outputs.
- Develop scalable methods to validate synthetic data, including failure-mode analysis, task mutation checks and trajectory auditing.
- Work with research engineers, domain experts and data vendors to diagnose quality problems and improve data generation.
- Turn research findings into production tools, dashboards, validation pipelines and feedback loops.
- Set rigorous standards for judging whether agent training data is useful, and mentor research engineers.
What you need
- At least 5 years of research or engineering experience building AI or machine learning data evaluation and quality systems.
- Prior experience leading technical teams or projects from problem definition through implementation and iteration.
- Advanced proficiency in Python, Docker and Linux environments.
- Strong background in AI evaluations, post-training, synthetic data validation, benchmarks or related evaluation infrastructure.
- Clear written communication, careful attention to detail, and experience working in an early-stage startup environment.
- Ability to work with subject-matter experts and convert their judgment into scalable review or data generation systems.
How to apply
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