OpenAI
Protection Scientist Engineer, Integrity
About this role
OpenAI is seeking a Protection Scientist Engineer to design and implement systems that detect and prevent misuse of AI products. This interdisciplinary role combines data science, machine learning, and investigation to monitor abuse, respond to critical escalations, and develop safety mitigations across OpenAI's product portfolio.
What you'll do
- Design and build abuse monitoring systems for new product launches and maintain monitoring for existing products
- Investigate critical escalations and abuse cases not caught by existing safety systems
- Prototype and productionize detection, review, and enforcement systems for major harms
- Develop and automate processes to scale monitoring operations
- Collaborate with product, policy, ops, and engineering teams to identify risks and implement solutions
- Participate in on-call rotation for urgent escalations outside normal work hours
What they're looking for
- SQL and Python for technical analysis and detection
- Trust and safety experience or work with policy and enforcement teams
- Data engineering (building tables, writing production pipelines)
- Machine learning principles and execution
- Process scaling and automation with language models
- Investigative mindset and analysis
- Basic software development and production code writing
- Cross-functional collaboration
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OpenAI
OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.
View all jobs at OpenAILikely interview questions
- Walk us through a time when you identified and investigated a new type of abuse or misuse in a product. What data and tools did you use, and how did you validate your findings?
- Describe your experience building data pipelines or core tables in production. What languages and tools did you use, and how did you ensure data quality?