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

Highlights

• Own self-serve platform hiding GPU complexity for training and inference.
• Operate multi-cloud GPU fleet powering hundreds of millions monthly queries.
• Make GPU failures boring with fault tolerance, autoscaling and deep observability.

What they offer

• Commission

Ideal candidate

Platform-minded infrastructure owner who thrives in ambiguity and loves hiding complex GPU operations behind simple self-serve systems for researchers and engineers.

Requirements

• Deep Kubernetes experience with custom operators, CRDs and multi-cluster federation
• Experience managing GPU clusters at scale with NVIDIA hardware, CUDA and fast networking
• Experience orchestrating 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++
• Experience supporting long-running training jobs and high-availability inference services
• Own problems end-to-end and thrive when the path forward is not laid out