At Avra, every technical IC is a Member of Technical Staff (MTS). The title doesn't put anyone in a silo: you own systems and outcomes, not steps in a function, and you keep building depth in your area. Seniority shows up in your scope, level, and compensation, not in titles.
🏢 About Avra
Our customers make real-time decisions based on our responses, so when we're down, their operations stop. Avra's cloud is just one more dataplane, alongside the dataplanes we operate inside customer environments — so observability and reliability have to work the same way everywhere.
🎯 The Role
In this role, you'll join the Platform team as our go-to expert on observability and reliability.
✅ Key Responsibilities
Evolve our observability stack for logs, metrics, traces, and alerting.
Make sure every dataplane, in our cloud and on-premise, reports its active release, health, heartbeat, logs, metrics, and usage to the control plane.
Bring telemetry into customer clusters within a model where agents only make outbound connections.
Detect drift between the desired state and what's actually running in each environment.
Monitor the health of our deployment and runtime agents.
Provide visibility into ephemeral workloads, such as the Ray clusters that run our batch inference.
Define SLOs, lead incident response and postmortems, and reduce MTTR — including when a fix requires coordinating with the customer.
Reduce telemetry cost: less redundant data, more useful signal.
📌 Required Qualifications
Deep experience with OpenTelemetry and observability backends.
Hands-on practice with SLOs, error budgets, actionable alerting, and incident management.
Strong experience with Kubernetes and infrastructure as code (Terraform / Helm).
Experience operating software in environments you don't fully control.
Production-quality code and reviews, and a willingness to operate what you build.
⭐ Desirable Experience
Shipping software to customer-hosted Kubernetes (e.g., Helm, outbound-only connectivity).
GCP or GKE, AWS or EKS.
ML multi-node/multi-cluster workloads in production.
