Manager, Software Engineering - Devex AI Tools

Figma Figma · Enterprise · Canada +1 · Engineering

Manager for an AI Tools team focused on building AI-powered workflows, platforms, and tooling for internal developer productivity. The role involves leading a team, owning technical strategy for AI Developer Experience, hiring, ensuring reliability and observability of cloud agent platforms, partnering with other teams, evaluating AI tools, and establishing evaluation frameworks for AI-generated outputs.

What you'd actually do

  1. Lead and grow a team of engineers responsible for building and operating Figma's AI developer workflows and the cloud agent platform that powers them
  2. Own the technical strategy and roadmap for AI Developer Experience, spanning sandbox runtime infrastructure, cloud agent reliability, workflow orchestration, and org-wide agentic workflows
  3. Hire and scale the team - establishing team culture, execution cadence, and operational processes from the ground up
  4. Drive the reliability, observability, and scalability of our cloud agent platform, ensuring it meets production-grade standards as adoption grows across the engineering organization
  5. Partner with product engineering, security, infrastructure, and DevEx teams to identify the highest-leverage opportunities for AI-assisted developer workflows and drive adoption

Skills

Required

  • 3+ years of experience managing infrastructure, platform, or developer experience engineering teams
  • Track record of scaling teams and delivering high-performing systems
  • Strong software engineering background, with Staff-level or above technical depth before moving into management
  • Deep understanding of distributed systems, platform architecture, and the operational challenges of running production infrastructure at scale
  • Demonstrated fluency with AI/ML systems, LLM-based workflows, or agent architectures - as a builder, not just a consumer
  • Ability to reason about tradeoffs in nondeterministic systems
  • Experience hiring, onboarding, and growing engineering teams in fast-paced, high-growth environments
  • Ability to set technical direction, drive cross-functional alignment, and make sound architectural decisions while balancing speed of execution with long-term sustainability

Nice to have

  • Direct experience building AI developer tooling, agent platforms, or AI-assisted engineering workflows
  • Familiarity with agent frameworks such as Cursor, Claude Code, or similar background agent systems, including MCP (Model Context Protocol) integrations
  • Experience with workflow orchestration platforms such as Temporal, n8n, or Tines for multi-stage automation
  • Background in developer experience platforms including CI/CD systems, build tools (Bazel/Blaze), or large polyglot monorepo environments
  • Experience building evaluation and quality frameworks for AI-generated outputs

What the JD emphasized

  • AI/ML systems
  • LLM-based workflows
  • agent architectures
  • nondeterministic systems
  • AI developer tooling
  • agent platforms
  • AI-assisted engineering workflows
  • agent frameworks
  • workflow orchestration platforms
  • AI-generated outputs

Other signals

  • AI-powered workflows
  • cloud agent platform
  • agentic CI auto-fixing
  • AI-assisted code review
  • background cloud agents
  • workflow orchestration
  • AI-assisted developer workflows