AI Success Engineer - EDU

About this role

OpenAI seeks an AI Success Engineer to serve as the primary technical advisor for education institutions adopting OpenAI's platform post-sale. You'll drive account health, identify high-impact use cases spanning academic and administrative workflows, and guide customers from deployment through scaled adoption while coordinating across internal teams.

What you'll do

• Manage post-sale technical relationships and act as trusted advisor on deployment, adoption, and value realization for education customers
• Lead technical enablement sessions, configure products, and oversee account-level deployment planning across multiple workstreams
• Identify and validate use cases by embedding with customer teams to map workflows and understand pain points
• Build relationships with provosts, CIOs, IT leaders, and academic stakeholders to align institutional goals with OpenAI capabilities
• Create adoption roadmaps with clear sequencing, milestones, and KPIs; facilitate workshops on best practices and institution-wide enablement
• Surface customer feedback and technical blockers to internal teams (Solutions Architecture, Product, Engineering) and coordinate across functions

What they're looking for

• Technical account management or customer success leadership (8+ years)
• Deep knowledge of OpenAI APIs, SDKs, embeddings, RAG, and fine-tuning approaches
• Program and project management with ability to lead multi-workstream initiatives
• Technical-to-business translation and executive communication
• Workflow mapping and requirements diagnosis
• Education sector experience (preferred)
• Change management and adoption strategy
• Cross-functional collaboration and stakeholder alignment

Benefits

• Based in San Francisco or New York with relocation assistance
• Work with world's most ambitious organizations across education, enterprise, and government
• Influence product direction through direct customer feedback and field insights
• Blend of technical, advisory, and program leadership responsibilities

Likely interview questions

• Walk us through a complex technical implementation you've led post-sale; how did you handle misalignment between customer expectations and product capabilities?
• Describe your hands-on experience with LLM APIs, embeddings, and RAG. How would you explain these concepts to non-technical faculty or administrators?