Technical Lead Manager, Forward Deployed Engineering

Handshake · Enterprise · San Francisco, CA · Engineering

Technical Lead Manager, Forward Deployed Engineering at Handshake AI. This player-coach role involves shipping end-to-end AI solutions for strategic partners, designing and building integrations, tooling, APIs, and workflows, and managing a small team of FDEs. The focus is on building production-ready systems and scaling team output through reusable components, while staying hands-on with coding.

What you'd actually do

  1. Own and ship end-to-end technical solutions directly for Handshake AI's highest-priority customer engagements.
  2. Design and build integrations, tooling, APIs, and workflows in close collaboration with partner technical teams, researchers, and operators.
  3. Serve as the primary technical point of contact for key accounts — understanding their systems deeply and delivering solutions that work in production.
  4. Write production-quality code and architect systems that are reliable, observable, secure, and maintainable.
  5. Manage and develop a small team of 3–5 Forward Deployed Engineers — set expectations, unblock, and grow them.

Skills

Required

  • 6+ years of software engineering experience, with meaningful depth in backend, fullstack, or systems work.
  • 2+ years in a customer-facing or field engineering role (e.g., forward deployed, solutions engineering, field engineering, customer-embedded product development).
  • Experience as a tech lead or TLM — setting technical direction for a team, not just owning your own work.
  • 1–2 years managing a small team of engineers (3–6); comfortable with feedback, growth conversations, and performance.
  • Ability to translate technical tradeoffs into business decisions; comfortable presenting options and recommendations to both technical and non-technical partners.
  • Strong stakeholder management skills; builds trust quickly with external partners and communicates clearly under pressure.
  • Strong instincts on triage and prioritization across multiple concurrent engagements.

Nice to have

  • Experience with AI/ML product integrations or production reliability in AI systems.
  • Familiarity with distributed systems and backend architecture at scale.
  • Experience building reusable platforms or internal tooling from bespoke customer solutions.
  • Background working with operations-heavy or research-adjacent organizations.

What the JD emphasized

  • Must be someone who codes regularly and wants to keep coding

Other signals

  • customer-facing AI solutions
  • integrations, tooling, APIs, and workflows
  • production-quality code and architect systems
  • managing and developing a small team
  • reusable components that scale the team's output