Manager, Software Engineering - AI Observability

Figma is hiring an Engineering Manager for Observability to lead the teams and platforms that monitor its production services and AI products. The role sets strategy for core and AI observability, improves instrumentation and data quality, and advances AI-driven operational capabilities while developing the engineering team.

Responsibilities:

• Lead and grow a 6 engineer team responsible for the reliability, scalability, and evolution of Figma's observability and AI observability platforms
• Own and evolve the AI observability ecosystem: a privacy-safe telemetry pipeline that aggregates trace, conversation, and model data, runs classification, and lets teams run evals to measure and improve AI feature quality
• Own Figma's core observability stack, including platforms like Datadog, ensuring high availability, strong data quality, and a healthy signal-to-noise ratio across metrics, logs, and traces
• Set the technical strategy for the instrumentation standards, libraries, agents, and operators that monitor services across the company
• Explore and ship AI-driven approaches to anomaly detection, root cause analysis, signal correlation, and operational automation
• Partner across infrastructure, product engineering, finance, and security to give teams clear visibility into system health at scale, including cost efficiency as the team's scope grows
• Coach and develop engineers through career growth, feedback, and technical leadership, building a culture of ownership and high-quality execution

Requirements:

• 4+ years of experience leading and growing engineering teams in infrastructure, observability, platform, or AI systems, with a track record of delivering reliable production systems at scale
• A strong foundation in distributed systems and a platform mindset: you build systems other teams adopt and rely on, not just for your own team
• Hands-on depth in observability, AI systems telemetry, or both, whether that means metrics, logs, and distributed tracing, or the traces, classification, and evals that power AI products
• Fluency with the modern tooling in your area, such as Datadog, OpenTelemetry, and Prometheus, or LLM tracing, prompt and model versioning, and eval workflows
• A high operational bar (instrumentation and data quality, SLO design, incident response) and the ability to set technical direction and drive cross-functional alignment in complex environments
• Breadth across both classic observability and AI observability, or the curiosity to grow into whichever is newer to you
• Experience creating AI Telemetry-assisted improvement feedback loops for services, in particular for AI products and features
• Experience improving infrastructure efficiency, including cost attribution, forecasting, vendor negotiation, and usage modeling
• A track record designing company-wide observability standards, shared libraries, and agent or operator-based integrations
• Experience applying AI or machine learning to anomaly detection, root cause analysis, or operational automation
• Experience scaling and mentoring high-performing teams through platform expansion or major architectural change