---
title: 'Member of Technical Staff - GPU Infrastructure at Prime Intellect'
canonical: 'https://feeny.ai/job/member-of-technical-staff-gpu-infrastructure-prime-intellect-san-francisco-yxe0m7p9mjqy'
type: 'job'
last_seen: '2026-09-07'
---

# Member of Technical Staff - GPU Infrastructure at Prime Intellect

- **Company:** Prime Intellect
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-07-08
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/primeintellect/297d925e-5a42-40bd-b02f-5c928d226f18

## 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.

Apply now and join us in our mission to democratize access to planetary scale computing.

## Compensation

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

## About Prime Intellect

## Company Overview
- **One-liner**: Prime Intellect provides an open, full-stack platform for companies to train, deploy, and continuously improve their own AI models through large-scale distributed reinforcement learning.
- **Entity Type**: Private (Series B)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2023
- **Founders**: Vincent Weisser (CEO), Johannes Hagemann (CTO)

## Core Business
- **Primary industry**: AI infrastructure, open-source AI research, and agentic training platforms
- **Target customers**: B2B — enterprises and AI labs that want to train custom models without building their own infrastructure; also open-source community contributors
- **Mission**: Democratize frontier AI training by making it accessible to every company and collectively owning the resulting open innovations.

## Products & Services
- **Compute**: Access to GPU clusters (H100, H200, B200+) from 50+ providers, plus on-demand single GPU instances and reserved clusters with InfiniBand networking and SLURM/K8s orchestration.
- **Hosted Training (Lab)**: Managed large-scale reinforcement learning (RL) training without infrastructure overhead, including a 2,500+ environment hub and custom eval benchmarks.
- **Inference**: Serverless or dedicated inference for custom models, with native LoRA support and 1-click deployment.
- **Open-Source Libraries**: `prime-rl` (async RL framework), `verifiers` (environments & evals), and environment hub for collaborative development.
- **INTELLECT-3**: A 100B+ parameter Mixture-of-Experts model trained on their RL stack, achieving state-of-the-art performance for its size.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Total Funding**: $70.44M across multiple rounds (CBInsights, 2026); earlier reports indicated $20.5M in seed rounds, followed by a Series B round of $49.94M led by Founders Fund and other investors.
- **Notable Investors/Partners**: Founders Fund, CoinFund, Distributed Global, Radical Ventures, Andrej Karpathy, Clem Delangue (Hugging Face), Dylan Patel (SemiAnalysis), Tri Dao (Together AI).
- **Growth Signals**: Headcount grew 128.6% YoY to 36 employees; 24 active job postings (monthly job growth +26.3%); launched INTELLECT-3 and SYNTHETIC-2 datasets; customers include Ramp and Zap; operates in 9 countries.

## Competitive Advantages
- **Full-stack integrated platform** – compute, post-training, evals, and inference in one place, reducing the fragmentation of building and maintaining AI workflows.
- **Large-scale distributed RL expertise** – proven ability to train 100B+ models across clusters (global training via their RL stack).
- **Open-source ethos** – models, datasets, and libraries are released openly, attracting a community of researchers and developers.
- **Founding team & research edge** – strong ties to frontier AI research, with contributions like prime-rl and self-improving agent loops.

## Strategic Focus
- **Self-improving agents** – building infrastructure that closes the loop from deployment back to training, enabling models to compound performance over time.
- **Scaling RL at extreme sizes** – recent research (RL at 1T Scale) shows commitment to massive-scale reinforcement learning.
- **Enterprise adoption** – adding dedicated solutions engineers and managed workflows for corporate customers.

## Why Work Here
- **Culture**: Seeks “the most ambitious developers” and emphasizes cutting-edge research and engineering. The team includes former engineers from Together AI, Aleph Alpha, and Anyscale.
- **Remote/Hybrid**: Remote-friendly with HQ in San Francisco.
- **Growth trajectory**: Rapidly scaling company (headcount up 128% YoY) with multiple open roles across engineering, research, and go-to-market.
- **Tech stack**: Python, PyTorch, CUDA, Kubernetes, SLURM, Grafana, and a broad modern AI toolchain – opportunity to work on infrastructure-level challenges.

## Sources
1. [primeintellect.ai](https://www.primeintellect.ai/)
2. [linkedin.com/company/primeintellect-ai](https://www.linkedin.com/company/primeintellect-ai)
3. [jobs.ashbyhq.com/PrimeIntellect](https://jobs.ashbyhq.com/primeintellect)
4. [cbinsights.com/company/prime-intellect](https://www.cbinsights.com/company/prime-intellect)
5. [docs.primeintellect.ai/introduction](https://docs.primeintellect.ai/introduction)

## Other roles at Prime Intellect

- [Head of Talent](https://feeny.ai/job/head-of-talent-prime-intellect-san-francisco-naf4r2rwx21x) — San Francisco, CA
- [Member of Technical Staff - Training Platform](https://feeny.ai/job/member-of-technical-staff-training-platform-prime-intellect-san-francisco-4btkkz3tw3ps) — San Francisco, CA
- [Member of Technical Staff - Sandbox Platform](https://feeny.ai/job/member-of-technical-staff-sandbox-platform-prime-intellect-san-francisco-qsr7247a01ar) — San Francisco, CA
- [Member of Technical Staff - Inference](https://feeny.ai/job/member-of-technical-staff-inference-prime-intellect-7syapkjxntgq)
- [Member of Technical Staff - Full Stack Software Engineer](https://feeny.ai/job/member-of-technical-staff-full-stack-software-engineer-prime-intellect-san-enhkkcn3cfge) — San Francisco, CA
- [Member of Technical Staff - Compute Platform](https://feeny.ai/job/member-of-technical-staff-compute-platform-prime-intellect-san-francisco-00b0zmttc2p0) — San Francisco, CA
- [Research Engineer - RL Infrastructure](https://feeny.ai/job/research-engineer-rl-infrastructure-prime-intellect-san-francisco-s38mrqzq4m59) — San Francisco, CA
- [Research Engineer - Reinforcement Learning](https://feeny.ai/job/research-engineer-reinforcement-learning-prime-intellect-san-francisco-q3tyazz9enkn) — San Francisco, CA
- [Research Engineer - Distributed Training](https://feeny.ai/job/research-engineer-distributed-training-prime-intellect-san-francisco-6anh0v1a8nqx) — San Francisco, CA
- [Open Application for Unconventional Talent](https://feeny.ai/job/open-application-for-unconventional-talent-prime-intellect-san-francisco-g7rn6nfnxcg1) — San Francisco, CA