Note: The job is a remote job and is open to candidates in USA. Nscale is the GPU cloud engineered for AI, providing cost-effective, high-performance infrastructure for AI start-ups and large enterprise customers. As a Principal Observability Platform Engineer, you'll own the technical direction of Nscale's observability platform, ensuring deep visibility into GPU clusters and AI workloads while driving architectural improvements and mentoring the team.
Responsibilities
- Own the technical strategy and architecture for observability across metrics, logs, traces, and alerting at scale
- Drive platform decisions that have multi-year impact: tooling, data models, ingestion patterns, retention, cardinality management
- Identify systemic gaps before they become incidents; design platforms that make failure visible and fast to diagnose
- Partner with SRE, infrastructure, and AI/ML teams to embed observability natively into how Nscale builds and operates
- Define standards and patterns that other engineers adopt, not by mandate, but because they're clearly better
- Mentor and technically grow the observability team; raise the ceiling on what the team can build and own
- Lead incident postmortems and use them to drive durable platform improvements
- Evaluate and introduce tooling that meaningfully improves signal quality, operational efficiency, or scalability, and retire what doesn't
Skills
- 8+ years in SRE, infrastructure engineering, platform engineering, or observability-focused roles
- You've operated observability infrastructure at serious scale. You know what breaks at 10x and you design for it
- You have a strong bias toward simplicity. You've seen over-engineered observability stacks collapse under their own weight and you build accordingly
- Deep hands-on experience with a significant subset of: Prometheus, Thanos, VictoriaMetrics, Grafana, Loki, Tempo, OpenTelemetry, ClickHouse, Elastic
- Strong engineering fundamentals, proficient in Python, Go, or similar; comfortable owning complex systems end to end
- Experience with Kubernetes at scale; familiarity with GPU infrastructure or HPC environments (Slurm) is a strong plus
- You can architect systems, write the code, review others' work, and explain the tradeoffs clearly, all in the same week
- Infrastructure-as-Code is default, not optional (Terraform, Ansible, or equivalent)
- You influence without authority. Teams want your opinion because it makes their work better
- Experience with high-volume streaming pipelines for observability data (Kafka, Vector, Fluent Bit, etc.)
- Background in AI/ML infrastructure observability: GPU utilisation, training job visibility, inference latency
- Prior experience defining observability strategy at an organisation level
Benefits
- Bonus
- Equity
- Commission programs
- Medical
- Dental
- Vision
- Flexible paid time off
- Parental leave
- Retirement plan participation
Company Overview