Member of Technical Staff - GPU Infrastructure

Role overview Design and deploy GPU infrastructure for customers running large-scale AI training, inference, and high-performance computing workloads. This customer-facing role combines cluster architecture and capacity planning with hands-on implementation, performance tuning, and operational support across hardware and software.

Responsibilities - Translate workload needs into GPU cluster architectures and capacity plans, including deployments ranging from hundreds to thousands of GPUs. - Recommend and implement infrastructure for training, inference, and HPC workloads, including orchestration and high-speed interconnects. - Configure and optimize production clusters using SLURM, Kubernetes, GPU drivers, and container runtimes. - Diagnose issues across hardware, Linux, networking, drivers, and software; improve utilization and inter-node performance. - Build monitoring, alerting, automation, runbooks, and operational documentation, and support critical deployments on call. - Explain technical recommendations to customer engineers and executives, and coordinate incident resolution with internal teams.

Requirements - At least three years of hands-on work with GPU clusters and HPC environments. - Production experience with SLURM and Kubernetes, plus InfiniBand configuration and troubleshooting. - Strong understanding of NVIDIA GPUs, CUDA, drivers, and GPU container runtimes. - Experience with infrastructure automation such as Ansible or Terraform, and scripting or systems programming in Python and Bash. - Knowledge of Linux performance tuning, AI network topologies, and power and cooling needs for dense GPU deployments. - A record of customer-facing technical leadership and resolving complex infrastructure issues.

Nice to have - Experience operating deployments with 1,000 or more GPUs, or relevant NVIDIA DGX, HGX, or SuperPOD certification. - Familiarity with distributed training frameworks such as PyTorch FSDP, DeepSpeed, or Megatron-LM, and ML profiling. - Experience with AMD MI300 or Intel Gaudi accelerators, or contributions to open-source HPC and AI infrastructure.

Benefits and work setup - Remote work or an office option in San Francisco; visa sponsorship and relocation support are available. - The stated cash range is $150,000–$300,000, with equity incentives, a professional development budget, and team off-sites and conference attendance.