Manager, Software Engineering - DevEx AI Tools
$258k - $376kFigma
Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! Figma's AI Tools team builds the AI-powered workflows, platforms, and tooling that make every engineer at the company more productive. From agentic CI auto-fixing and AI-assisted code review to background cloud agents that turn a Slack message into a pull request, this team owns the systems that are transforming how Figma builds software. The team operates across a three-layer platform stack—sandbox runtime, cloud agents, and workflow orchestration—while shipping and maintaining a growing portfolio of org-wide developer workflows that compound across hundreds of engineers. This is a full time role that can be held from one of our US hubs or remotely in the United States.
What you’ll do at Figma:
- 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
- Own the technical strategy and roadmap for AI Developer Experience, spanning sandbox runtime infrastructure, cloud agent reliability, workflow orchestration, and org-wide agentic workflows
- Hire and scale the team - establishing team culture, execution cadence, and operational processes from the ground up
- Drive the reliability, observability, and scalability of our cloud agent platform, ensuring it meets production-grade standards as adoption grows across the engineering organization
- Partner with product engineering, security, infrastructure, and DevEx teams to identify the highest-leverage opportunities for AI-assisted developer workflows and drive adoption
- Evaluate and integrate emerging AI tools, models, and agent frameworks, making strategic bets on which capabilities to build versus buy
- Establish evaluation frameworks to measure the quality, cost, and impact of AI-generated code and agentic workflows across the organization
- Coach and mentor engineers through career development, performance feedback, and technical leadership, fostering a culture of ownership, speed, and high-quality execution
We'd love to hear from you if you have:
- 3+ years of experience managing infrastructure, platform, or developer experience engineering teams, with a 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 with 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 - with the 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
While not required, it’s an added plus if you also 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
- Holding interviews in an accessible location
- Enabling closed captioning on video conferencing
- Ensuring all written communication be compatible with screen readers
- Changing the mode or format of interviews
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