Principal Engineer - Observability

Weights & Biases Weights & Biases · Data AI · Bellevue, WA +2 · Technology

Principal Engineer, Observability to lead the architecture, development, and operations of an Observability platform for AI workloads at scale. The role involves defining how customers monitor, troubleshoot, and operate their AI workloads, working with engineering leaders to drive a unified Observability experience across metrics, logs, traces, and customer-facing insights. Key responsibilities include leading strategy and roadmap, designing telemetry pipelines, building customer-facing dashboards and alerts, driving reliability, and mentoring engineers.

What you'd actually do

  1. Lead the Observability strategy and roadmap, ensuring clear alignment with business goals, product direction, and performance/SLA objectives
  2. Design and implement low-latency, high-scale telemetry pipelines and data stores solutions that power observability across all CoreWeave products
  3. Build customer-facing experiences—dashboards, alerts, and workflows—that enable rapid troubleshooting and deep insight into AI workloads and platform health
  4. Drive reliability, durability, and self-healing across the Observability stack, owning key services in production and setting a high bar for operational excellence
  5. Shape customer experience for visibility by defining standards for metrics, SLOs, and dashboards that expose system performance and reliability in a clear, actionable way

Skills

Required

  • 15+ years of experience building and operating distributed systems at scale
  • strong track record of reliability-focused engineering
  • Proven experience leading Observability platform engineering initiatives and delivering products that directly address customer needs
  • Strong proficiency in one or more programming languages such as Go, Python, or Rust
  • comfort working in large-scale production environments
  • Deep understanding of distributed observability systems and telemetry pipelines (e.g., ClickHouse or similar technologies for telemetry at scale)
  • Strong understanding of cloud-native infrastructure—Kubernetes, scalable architectures, automation, and modern CI/CD and deployment practices
  • Excellent analytical and problem-solving skills
  • Exceptional communication and collaboration skills
  • Prior experience building end-to-end Observability solutions (metrics, logs, traces, dashboards, alerting) in production environments

Nice to have

  • Experience with modern AI platforms and workloads (e.g., large-scale training/ inference, GPU-based infrastructure, MLOps tooling) is a plus

What the JD emphasized

  • AI workloads at scale
  • observability across all CoreWeave products
  • customer-facing insights
  • low-latency, high-scale telemetry pipelines
  • distributed systems at scale
  • reliability-focused engineering
  • Observability platform engineering initiatives
  • modern AI platforms and workloads