Member of Technical Staff - GPU Infrastructure

Stack

Responsibilities

• ▹Partner with clients to understand workload requirements and design optimal GPU cluster architectures
• ▹Create technical proposals and capacity planning for clusters ranging from 100 to 10,000+ GPUs
• ▹Develop deployment strategies for LLM training, inference, and HPC workloads
• ▹Present architectural recommendations to technical and executive stakeholders
• ▹Deploy and configure orchestration systems including SLURM and Kubernetes for distributed workloads
• ▹Implement high-performance networking with InfiniBand, RoCE, and NVLink interconnects
• ▹Optimize GPU utilization, memory management, and inter-node communication
• ▹Configure parallel filesystems (Lustre, BeeGFS, GPFS) for optimal I/O performance
• ▹Tune system performance from kernel parameters to CUDA configurations
• ▹Serve as the primary technical escalation point for customer infrastructure issues
• ▹Diagnose and resolve complex problems across hardware, drivers, networking, and software
• ▹Implement monitoring, alerting, and automated remediation systems
• ▹Provide 24/7 on-call support for critical customer deployments
• ▹Create runbooks and documentation for customer operations teams

Requirements

• ▹3+ years of hands-on experience with GPU clusters and HPC environments
• ▹Deep expertise with SLURM and Kubernetes in production GPU settings
• ▹Proven experience with InfiniBand configuration and troubleshooting
• ▹Strong understanding of NVIDIA GPU architecture, the CUDA ecosystem, and driver stack
• ▹Experience with infrastructure automation tools (Ansible, Terraform)
• ▹Proficiency in Python, Bash, and systems programming
• ▹Track record of customer-facing technical leadership
• ▹NVIDIA driver installation and troubleshooting (CUDA, Fabric Manager, DCGM)
• ▹Container runtime configuration for GPUs (Docker, Containerd, Enroot)
• ▹Linux kernel tuning and performance optimization
• ▹Network topology design for AI workloads
• ▹Understanding of power and cooling requirements for high-density GPU deployments

Nice to have

• ▹Experience with 1,000+ GPU deployments
• ▹NVIDIA DGX, HGX, or SuperPOD certification
• ▹Familiarity with distributed training frameworks (PyTorch FSDP, DeepSpeed, Megatron-LM)
• ▹ML framework optimization and profiling
• ▹Experience with AMD MI300 or Intel Gaudi accelerators
• ▹Contributions to open-source HPC/AI infrastructure projects

Soft skills

What we offer

• ▹Cash compensation range of $150-300k plus equity incentives

About the company

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Its platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system. The company is backed by leading investors including Founders Fund, Radical Ventures, and NVIDIA.