Senior / Staff Software Engineer (Observability / SRE)
You will..
- Design and lead the architecture and development of Waabi’s monitoring and observability stack, used to monitor the health and performance of cloud and on-prem environments.
- Develop and extend workloads and benchmarks (compute, storage, network, ML/AI) and integrate stress, chaos, and regression tests to validate hardware and platform choices.
- Analyze and optimize end-to-end performance across hardware, firmware, Linux kernel, runtimes, and distributed services using advanced profiling tools (perf, eBPF, flamegraphs, tracing frameworks).
- Build automation and observability tooling (Go/Python/Java, Kubernetes/Docker) for CI/CD-based performance regression detection, telemetry, alerting, and anomaly detection.
- Work with client teams to support their applications’ observability requirements.
- Influence system architecture and tooling decisions that improve how Waabi builds, monitors, and scales its infrastructure.
- Drive execution and quality, writing design docs, setting milestones, mentoring ICs, and communicating insights and results to stakeholders and leadership.
Qualifications:
- 5+ years software engineering or systems/performance engineering experience (BS in CS/EE or related), with demonstrated end-to-end ownership of complex projects.
- Proficient in at least one of: Python, Rust, C/C++; strong CS fundamentals and system design skills.
- Hands-on with Linux internals (CPU scheduling, memory, I/O, networking) and perf tooling (perf, eBPF, flamegraphs, tracing frameworks).
- Experience with Kubernetes, microservices, and distributed systems; comfort building production services and pipelines.
- Proven track record of clear communication, writing design docs, and leading cross-functional efforts.
Bonus:
- Experience deploying and managing observability platforms (OpenTelemetry, Grafana OSS).
- Performance tuning for databases/streaming/batch/ML platforms; GPU/xPU or Arm performance exposure.
- Experience tuning stream processing, batch or ML platforms (e.g. Argo Workflows, PyTorch).
- Familiarity with microservices debugging and distributed tracing (OpenTelemetry, Prometheus).