Member of Technical Staff - GPU Infrastructure
$150k - $300k • Remote • San Francisco • FullTime
Posted 3mo ago
About the job
Prime Intellect is building the open superintelligence stack, a full-stack system that unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. We are seeking a Member of Technical Staff focused on GPU Infrastructure to join our team. This customer-facing role requires deep technical expertise to design, deploy, and optimize GPU cluster architectures for AI labs and ambitious AI teams. You will work at the intersection of frontier research, real infrastructure, and go-to-market for a new category of AI computing.
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 primary technical escalation point for customer infrastructure issues.
- Diagnose and resolve complex problems across the full stack - 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 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, 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.
- Power and cooling requirements for high-density GPU deployments.
Benefits
- Cash Compensation Range of $150-300k
- Equity Incentives