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Manager, Software Engineering - DevEx AI Tools

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Gravity Engineering Services Pvt Ltd.
Full Time position
Listed on 2026-06-08
Job specializations:
  • Software Development
    AI Engineer, Software Engineer, DevOps, Cloud Engineer - Software
Salary/Wage Range or Industry Benchmark: 150000 - 200000 USD Yearly USD 150000.00 200000.00 YEAR
Job Description & How to Apply Below

About the Role

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 Dev Ex 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
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