Site Reliability Engineer

Baseten is a company that powers mission-critical inference for dynamic AI companies. They are seeking a Site Reliability Engineer to define and codify standards for day 2 operations of their ML infrastructure platform, ensuring reliability and empowering the organization to operate confidently.

Responsibilities:

• Own the reliability of Baseten's multi-cloud Kubernetes infrastructure, including incident response, post-mortems, and remediation tracking
• Build and maintain observability infrastructure — metrics, logging, dashboards, and alerting — as code
• Author, validate, and improve runbooks for recurring failure patterns, ensuring they're structured for low-context, safe execution
• Identify high-frequency failure patterns and convert them into automated mitigations or self-healing automations
• Diagnose and resolve runtime issues related to latency, memory behavior, GPU utilization, concurrency, and model lifecycle management
• Define and instrument SLOs and SLIs across customer workloads and internal services
• Navigate ambiguity, make principled tradeoffs, and avoid unnecessary complexity in the systems you build and the processes you define

Requirements:

• Extensive hands-on experience with Kubernetes (multi-cloud experience across EKS, GKE, or similar is a strong plus)
• Experience in building and maintaining scalable infrastructure
• Strong foundation in observability tooling: metrics (VictoriaMetrics, Prometheus), logging (Loki, ELK), dashboards (Grafana), and alerting pipelines. Observability-as-code experience is a plus
• Experience with infrastructure-as-code (Terraform, Helm) and GitOps workflows (Flux CD, ArgoCD)
• Experience writing and improving runbooks, leading incident response, and doing post-mortem analysis
• Comfort working at the intersection of engineering and operations — you write code, but you also think deeply about process, escalation paths, and operational leverage
• Familiarity with incident management platforms (incident.io or similar) is a plus
• No prior ML experience required, but curiosity about how ML models are deployed and served at scale will serve you well