Senior Engineering Manager, AI Product (Thunderbolt)

Engineering Execution:

• Lead engineering execution by clarifying priorities, ownership, timelines, risks, dependencies, and quality expectations.
• Manage engineers and contractors, including coaching, feedback, prioritization, performance support, and team development.
• Stay close to the codebase by contributing code, reviewing technical work, and helping the team maintain momentum and quality.

Planning and Collaboration:

• Partner with technical and product leadership to turn product direction, AI strategy, and user experience goals into coordinated engineering plans.
• Establish planning, review, release, and follow-through practices that improve predictability and reduce bottlenecks.
• Build strong product and architectural context across AI-enabled workflows, integrations, and customer-controlled architectures.

Team Growth and Standards:

• Maintain high standards for code quality, testing, documentation, reliability, security awareness, maintainability, and release readiness.
• Lead hiring, onboarding, and resource planning as the Thunderbolt engineering team grows.
• Help Thunderbolt scale from an incubation-stage product into a reliable, enterprise-ready open-source AI product.