Why This Job is Featured on The SaaS Jobs
This role stands out in the SaaS landscape because it sits directly on the boundary between product deployment and responsible operations for highly capable AI systems. In modern SaaS, trust is increasingly a feature, especially when offerings process sensitive user inputs and operate under real adversarial pressure. The focus on privacy-preserving safety mechanisms reflects a broader shift toward building governance and safeguards into the platform layer rather than treating them as after-the-fact compliance work.
For a long-term SaaS career, the work maps to durable problems that recur across categories: monitoring at scale, minimizing human review while maintaining accountability, and designing systems that can be audited without leaking user data. Experience translating ambiguous risk domains into deployable frameworks is particularly portable across SaaS teams that ship ML-enabled products, operate marketplaces, or handle regulated data. The intersection of privacy, security, and safety also aligns with how SaaS organizations increasingly structure cross-functional risk ownership.
The role is best suited to professionals who like research that lands in production systems and who are comfortable formalizing messy real-world behaviors into testable mechanisms. It will fit someone motivated by adversarial thinking, rigorous evaluation, and building infrastructure that other product teams rely on as models and usage expand.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Team:
Our Safety Systems org ensures that OpenAI’s most capable models can be responsibly developed and deployed. We build evaluations, safeguards, and safety frameworks that help our models behave as intended in real-world settings.
About the Role:
We are seeking a Researcher in Privacy-Preserving Safety to help design and build the next generation of privacy-preserving safety systems for frontier AI models. This role sits at the intersection of AI safety, security, and privacy, with a focus on developing auditable, privacy-first mechanisms that enable robust harm detection and mitigation without exposing sensitive user data.
You will help define and operationalize frameworks for identifying and addressing frontier risks (e.g., bioweapon instructions, malware creation, suicide/self-harm risks, jailbreaks), while ensuring that privacy guarantees remain intact—even under adversarial conditions.
This role is central to our long-term goal of scaling our automated privacy-preserving safety systems to mitigate potential harms while minimizing human review.
You’ll work on foundational problems such as privacy-preserving monitoring, algorithmic auditing, secure enclaves, and adversarially robust safety enforcement protocols, helping ensure that safety systems scale without compromising user trust.
In this role, you will:
Design and implement privacy-first architectures for detecting and mitigating harmful model behaviors.
Build frameworks for auditable private identification of high-risk content (jailbreaks, cyber threats, or weaponization instructions).
Develop strict, auditable mechanisms triggered only by harm signals.
Drive the development of automated safety systems that preserve privacy at every level.
You might thrive in this role if you:
Are a researcher with deep interest in privacy, security, and AI safety, motivated by building systems that are both trustworthy and effective at scale.
Hold a PhD or equivalent experience in Computer Science, Cryptography, Security, Machine Learning, or related fields
Have the ability to translate ambiguous problem spaces into formal frameworks and deployable systems
Demonstrate profiency in one or more of:
Privacy-preserving computation (e.g., secure enclaves, MPC, differential privacy)
Security and adversarial systems
Machine learning safety or alignment
Experience designing robust systems under adversarial threat models
Have experience with AI safety, jailbreak detection, or model alignment
Are familiar with privacy-preserving machine learning techniques, algorithmic auditing and/or secure system design
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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