Technical Lead Manager, Data Engineering, Trust & Safety

ABOUT THE TEAM

The Applied team brings OpenAI’s technology to the world through products used by hundreds of millions of people and by developers and businesses building on our APIs. We work across research, engineering, product, policy, safety, and operations to deploy frontier AI systems responsibly and safely.

The Trust & Safety Data Engineering team builds the data foundations that help OpenAI understand, detect, investigate, and mitigate abuse and safety risks across our products. We partner with Integrity, Investigations, Safety Systems, Product Policy, Privacy, Data Science, Engineering, and Data Platform to create reliable, privacy-safe datasets and pipelines for fraud and abuse detection, enforcement workflows, safety measurement, ML feature generation, launch readiness, and transparency reporting.

ABOUT THE ROLE

We are hiring a Technical Lead Manager to lead and grow the Trust & Safety Data Engineering team. This is a hands-on leadership role for someone who can set strategy, shape data architecture, align senior stakeholders, coach engineers, and drive execution on high-impact data systems.

You will help turn fragmented launch and incident support into durable, reusable, privacy-safe data foundations that Trust & Safety teams can rely on. The systems your team builds will help OpenAI detect risk, investigate abuse, power operational workflows, develop and evaluate safety models, measure interventions, support product launches, and report accurately on platform integrity.

IN THIS ROLE, YOU WILL

- Lead and grow a high-performing Trust & Safety Data Engineering team.

- Define the roadmap and technical strategy for Trust & Safety data systems.

- Build canonical, privacy-safe datasets and pipelines for abuse detection, fraud detection, risk signals, enforcement, scaled review, transparency reporting, and safety monitoring.

- Create reusable foundations for Trust & Safety model development, including features, labels, training data, backtesting, evaluation, and production inputs.

- Establish ownership, documentation, data quality standards, monitoring, and operational rigor for critical datasets and workflows.

- Reduce dependence on sensitive raw logs by building structured alternatives with appropriate access controls, retention, deletion semantics, and governance.

- Partner with Trust & Safety, Product, Policy, Privacy, Data Science, Engineering, and Data Platform on launch readiness, operational systems, and safety measurement.

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