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.

• Raise the bar for technical judgment, prioritization, communication, and execution in a fast-moving environment.

You Might Thrive in This Role If You

• Have led data engineering teams that build and operate production data systems at scale.

• Experience in trust and safety, integrity, abuse prevention, fraud, investigations, risk operations, safety systems, privacy, or adjacent domains.

• Are deeply technical and comfortable with data architecture, modeling, pipelines, reliability, privacy, and operational tradeoffs.

• Have experience with large-scale data systems such as Spark, Airflow or similar orchestration systems, distributed storage, batch/streaming pipelines, and modern warehouse patterns.

• Think of data as a product: reliable, documented, governed, observable, discoverable, and designed for repeated use.

• Can create clarity in ambiguous problem spaces and make principled tradeoffs quickly.

• Have a strong track record partnering with senior stakeholders across engineering, data science, operations, policy, privacy, product, or executive teams.

• Have hired, developed, and retained senior engineers.

• Are motivated by building systems that make frontier AI products safer and more trustworthy.

Nice to Have

• Experience supporting ML systems through feature engineering, training data, labels, model evaluation, or production model pipelines.

• Experience with launch readiness, monitoring, alerting, incident response, semantic layers, metrics governance, or executive-facing reporting.

Workplace & Location

This role is based in our San Francisco HQ. We offer relocation assistance to new employees.

Please note: this role may involve work related to sensitive or concerning safety, abuse, fraud, or user-risk domains. Strong discretion, judgment, and resilience are essential.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. 

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

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