Requirements

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

Nice to Haves

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

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