Staff Software Engineer, Node Infra
Leads technical strategy for node lifecycle management, scales AI clusters across clouds, and builds automated hardware health systems for large-scale accelerator fleets. Requires deep expertise in distributed systems, cloud platforms, systems programming, and ML accelerators.
Key Responsibilities
- Own the technical strategy and roadmap for node lifecycle management - ingestion, bring-up, health checking, and automated repair
- Drive cross-team initiatives to build and scale AI clusters across multiple clouds and accelerator families
- Design and operate the systems that detect, isolate, and remediate unhealthy hardware automatically, driving up fleet MTBI and minimizing stranded capacity
- Define infrastructure architecture, ensuring the hardest problems get solved - whether by you directly or by working through others
- Work closely with cloud providers and internal research/inference/product teams to shape long-term compute, data, and infrastructure strategy
- Establish and evolve operational excellence practices (incident response, postmortem culture, on-call)
- Support the growth of engineers around you through technical mentorship and coaching
Minimum Qualifications
- Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP/Azure)
- Strong proficiency in at least one systems language (e.g., Rust, Go, or Python), IaC proficiency with Terraform
- Hands-on experience with machine learning accelerators (GPUs, TPUs, or Trainium)
- Track record of leading complex, multi-quarter technical initiatives that span multiple teams or systems
- Ability to build alignment across senior stakeholders and communicate effectively at all levels
Preferred Qualifications
- 8+ years of software engineering experience, including time as a technical lead setting direction for a team
- Experience managing large scale compute infrastructure at hyperscale (10K+ nodes), including capacity management and efficiency
- Depth in one or more of: Kubernetes internals (scheduler, autoscaler, kubelet, Karpenter), cluster orchestration systems (Mesos, Borg-like), or node provisioning pipelines
- Low-level systems experience: kernel, virtualization, device drivers, firmware, or hardware health/diagnostics daemons
- Familiarity with high-performance networking (EFA, RDMA, InfiniBand) for distributed ML workloads
- Demonstrated ownership of production reliability for high-throughput, latency-sensitive systems
- Contributions to relevant open-source projects (Kubernetes, Linux kernel, container runtimes, etc.)
- Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems
Compensation
Annual Salary: $320,000—$405,000 USD
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