Staff Site Reliability Engineer – Automation and Platform

Key Responsibilities

• Define and implement a robust strategy for delivering and running software reliably and at scale across multiple datacenters and cloud-based solutions.
• Architect self-service platforms and internal tooling that let product teams, external customers, and cluster operators safely trigger and observe critical workflows with minimal handoffs.
• Define and evolve reliability practices for inference workloads, including SLOs and SLIs for latency, throughput, and accuracy stability; error budgets; blameless postmortems; chaos testing; and capacity forecasting across multi-datacenter and on-prem environments.
• Mentor mid-level SREs, support critical incident escalations, and use production pain points to prioritize the highest-leverage automation work.
• Measure and drive impact through clear metrics, including toil reduction, deployment velocity, SLO compliance, MTTR, and adoption of self-service workflows.

Required Experience & Skills

• 8+ years in SRE, infrastructure engineering, or platform engineering, with a strong record of improving automation and reliability at large scale in FAANG, hyperscaler, or similarly demanding environments.
• Deep expertise operating large scale heterogenous clusters with a proprietary cloud control plane.
• Proven track record designing and delivering CI/CD or GitOps systems using Argo CD or similar tools, with strong safety and observability built in.
• Hands-on experience with observability systems such as Loki, Tempo, Mimir, and Prometheus.
• Ability to lead complex projects end to end, influence cross-functional stakeholders, and communicate technical direction clearly.

Nice-to-Haves

• Experience with Bazel or other large-scale build systems in production.
• Background in AI/ML inference systems, including model serving runtimes, GPU or wafer-scale orchestration, latency and accuracy SLOs, or drift monitoring.
• Prior work on predictive autoscaling, chaos engineering, or cost-aware capacity planning for compute-intensive workloads.