Member of Technical Staff (Software Engineer, GPU Cluster Infrastructure)

Highlights

• Power hundreds of millions of real-time AI queries a month.
• Own a unified self-serve platform for training and inference workloads.
• Make GPU failures boring with fault tolerance and autoscaling.

What they offer

• Commission

Ideal candidate

End-to-end owner who thrives in ambiguity and loves hiding infrastructure complexity behind reliable platforms that keep large-scale AI workloads running.

Requirements

• Deep Kubernetes experience with custom operators, CRDs and multi-cluster federation
• Managed GPU clusters at scale with NVIDIA hardware, CUDA and InfiniBand or RoCE networking
• Orchestrated compute across multiple clouds such as CoreWeave, AWS and GCP
• Strong distributed systems fundamentals in scheduling, resource allocation and fault tolerance
• Write infrastructure and systems-level code in Go, Rust or C++
• Supported long-running training jobs and high-availability inference services
• Own problems end-to-end and thrive when the path forward is not laid out