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Member of Technical Staff - GPU Infrastructure

United States, San Francisco · Job Posted February 21, 2026
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Job Description

Prime Intellect is building the open superintelligence stack - from frontier agentic models to the infra that enables anyone to create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full rl post-training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups and enterprises to run end-to-end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts. As our Solutions Architect for GPU Infrastructure, you'll be the technical expert who transforms customer requirements into production-ready systems capable of training the world's most advanced AI models.

Job Responsibility

  • 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

Nice to have

  • Experience with 1000+ GPU deployments
  • NVIDIA DGX, HGX, or SuperPOD certification
  • 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

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