OpenAI
Full Stack Engineer, Intelligence Systems
About this role
OpenAI seeks a Fullstack Engineer to develop intelligence systems that detect and prevent AI abuse. You'll build scalable tools integrating OpenAI's models to help internal teams analyze large datasets, investigate threats, and inform safety decisions across the company's products.
What you'll do
- Prototype and build at-scale systems to detect, triage, and monitor signals from open-source and internal data
- Deliver end-to-end solutions for complex intelligence challenges while ensuring code quality and scalability
- Act as technical bridge between Intelligence team and Platform Integrity, Safety Systems, and Research teams
- Design user interfaces for non-technical investigators and analysts
- Automate team workflows and route leads through new application prototypes
- Create impact reports using data analysis and visualization products
What they're looking for
- Full-stack software engineering and systems design
- SQL and Python programming
- Data analysis and visualization
- Security, intelligence, or abuse detection domain experience
- Project management and cross-functional collaboration
- Large-scale dataset handling and processing
- AI techniques for problem-solving
- UI/UX design for analytical tools
Benefits
- Hybrid work model (3 days in office per week)
- Relocation assistance provided
- Work on AI safety and responsible AI deployment
- Collaborate with leading AI research teams
- Contribute to protecting AI systems from misuse
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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 you built an end-to-end system to detect or prevent abuse. What technical challenges did you face and how did you scale it?
- Describe your experience working with large, open-domain datasets. What tools and languages did you use for data analysis and what insights did you surface?