Software Engineer, Privacy Infrastructure
About the Team OpenAI’s Privacy Engineering team sits at the intersection of Security, Privacy, Legal, and Core Infrastructure. Our mission is to build data infrastructure and systems to support our privacy, legal, and security teams—securely, quickly, and at scale.
What this role actually needs.
Software Engineer, Privacy Infrastructure at OpenAI in San Francisco. UpJobz keeps this listing high-signal for applicants targeting serious high-tech roles across the United States, Canada, and Mexico. About the Team OpenAI’s Privacy Engineering team sits at the intersection of Security, Privacy, Legal, and Core Infrastructure. Our mission is to build data infrastructure and systems to support our privacy, legal, and security teams—securely, quickly, and at scale.
Day-to-day expectations
A clear list of the work this role is designed to cover.
- Design and operate data storage pipelines that can operate at scale.
- Build search & discovery services (e.g., Spark/Databricks, index layers, metadata catalogs) based on the needs of partner teams.
- Automate secure data transfers—encrypting, checksumming, and auditing exports to reviewers.
- Stand up locked‑down compute environments that balance usability with security controls.
- Instrument monitoring and KPIs that maintain accountability of data holds and productions.
- Collaborate cross‑functionally to codify SOPs, threat models, and chain‑of‑custody documentation that withstand scrutiny.
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Next moves
- Tailor your resume around ai and llm instead of sending a generic application.
- Use the first two bullets of your application to connect your background directly to software engineer, privacy infrastructure is a high-signal hybrid role in san francisco, and it is most realistic for united states residents.
- Open the role quickly if it fits and bookmark three similar jobs before you leave the page.
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- $230K - $325K is visible, so calibrate your application around the posted range.
- Use united states residents as part of your positioning so the recruiter does not have to infer it.
- Show concrete examples of succeeding in hybrid environments.
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