OpenAI
AI Support Engineer, Biosciences - San Francisco
About this role
OpenAI seeks an experienced Support Engineer to build and lead support operations for GPT-Rosalind and life sciences customers. You'll own complex technical cases, create support playbooks and escalation paths, coordinate cross-functional resolution, and help define how OpenAI supports scientific organizations adopting AI systems.
What you'll do
- Own complex technical and operational support cases from intake through resolution for life sciences customers
- Diagnose issues with onboarding, workspace creation, API configuration, model availability, and product setup
- Build intake flows, diagnostic procedures, escalation paths, playbooks, and knowledge content for bioscience workflows
- Route issues appropriately between technical support, solutions architecture, scientific consultation, and product feedback
- Coordinate escalations across Product, Engineering, Security, Legal, and subject-matter experts
- Identify recurring issues and drive durable fixes, documentation improvements, and product enhancements
What they're looking for
- Technical support and troubleshooting expertise
- Life sciences domain knowledge (computational biology, bioinformatics, genomics, or drug discovery)
- API and enterprise configuration management
- Cross-functional coordination and escalation management
- Root-cause analysis and problem diagnosis
- Knowledge management and process documentation
- Customer communication and boundary-setting
- AI and automation for support operations
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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
- Tell us about your experience supporting enterprise customers through complex technical configuration issues—what was the most challenging problem you resolved?
- How have you approached learning and supporting workflows in unfamiliar technical or scientific domains?