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
San Francisco; Remote Hybrid Full-time $150-300k
Prime Intellect is hiring a Member of Technical Staff - GPU Infrastructure
About company
Prime Intellect is building the open superintelligence stack for training, deploying, and continuously improving AI models. The platform integrates compute, training, inference, and sandboxing into a unified system designed for agentic workflows and large-scale reinforcement learning.
The company offers Lab, a hosted training environment where users can post-train their own self-improving agents using reinforcement learning across thousands of open-source RL environments. It supports scalable training workflows with full visibility, hands-on research support, and tools like Verifiers and Prime-RL for creating and evaluating agent tasks.
Prime Intellect also provides dedicated and serverless inference with native LoRA support, enabling one-click deployment of fine-tuned models. Its inference stack allows teams to serve custom models efficiently while capturing production traces to feed back into future training runs for continuous improvement.
Backed by Founders Fund, NVIDIA, Intel, and leading AI figures like Andrej Karpathy and John Schulman, Prime Intellect powers companies like Ramp and Zapier in building proprietary, cost-effective AI agents tailored to specific business workflows.