Wonderschool
Head of AI Agent Systems
San Francisco · Exec
Sponsorship not specifiedDetected 83 days ago
DevOpsLLMsA/B TestingCustomer SupportResearchLeadership
About the role
- You will work directly in the codebase, agent system, and infrastructure while leading a small team of engineers.
- You will be responsible for making the system work in practice and tying it to business outcomes.
Responsibilities
- Drive Revenue Through Agent Systems
- Design and deploy agent-driven workflows that directly impact revenue generation
- Partner with product and go-to-market to identify high-leverage revenue opportunities and implement them through agent systems
- Own Agent System Architecture and Performance
- Own the architecture of the multi-agent system including OpenClaw, Claude Code, GBrain, and internal tooling
- Design evaluation systems to measure success rates, failure modes, and reliability
- Build a Reliable Multi-Agent Execution System
Requirements
- Experience working with AI systems, LLMs, or automation in a real environment
- Experience debugging complex systems and improving reliability
- Ability to work directly in code and system architecture
- Ability to operate in a fast-paced, high-ownership environment
Nice to have
- Experience with workflow automation or internal platforms
- Experience working with regulated systems or compliance-heavy environments
- Background as a founder or early engineer at a startup
- Someone who can take a messy system and make it reliable
- Someone who connects technical systems to business outcomes and cares about revenue
- Someone who moves quickly and focuses on outcomes
- Someone who is comfortable working directly with leadership and making decisions
- What This Role Is Not
Company info
- This role is responsible for turning a partially working agent system into a production-grade platform that directly drives revenue by improving how we acquire customers, onboard them, operate their businesses, and iterate on product.
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This listing is sourced directly from Wonderschool's careers page and normalized into a canonical job model.