Member of Technical Staff - GPU Infrastructure

Primeintellect; San Francisco (CA); On-site; USD 150,000 - 300,000; Full time

Job summary

Prime Intellect in San Francisco is seeking a senior infrastructure engineer to design and deploy GPU-heavy workloads at scale. You will work directly with customers to translate workload requirements into robust GPU clusters and deployment strategies.

The role combines hands-on engineering with high-visibility architecture discussions, requiring deep knowledge of HPC tooling, networking, and production reliability.

Qualifications

• 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.

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.

Skills

GPU clusters and HPC environments

customer-facing technical leadership

Python

Bash

systems programming

CUDA ecosystem

driver stack

Tools

SLURM

Kubernetes

InfiniBand

NVIDIA CUDA

Docker

Containerd

Enroot

Terraform

Ansible

Job description

Own Your Intelligence

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post‑training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open‑source models trained end to end for long‑horizon tasks like autonomous research, and the full‑stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go‑to‑market for a category that does not fully exist yet.

Core Technical Responsibilities

This customer‑facing role combines deep technical expertise with hands‑on implementation. You'll be instrumental in:

Customer Architecture & Design

• 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

Infrastructure Deployment & Optimization

• 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

Production Operations & Support

• 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

Technical Requirements

Required Experience

• 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

Infrastructure Skills

• 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

Growth Opportunity

You’ll work directly with customers pushing the boundaries of AI, from startups training foundation models to enterprises deploying massive inference infrastructure. You’ll collaborate with our world‑class engineering team while having direct impact on systems powering the next generation of AI breakthroughs.

We value expertise and customer obsession - if you’re passionate about building reliable, high‑performance GPU infrastructure and have a track record of successful large‑scale deployments, we want to talk to you.

Compensation

Cash Compensation Range of $150-300k plus Equity Incentives