Member of Technical Staff - GPU Infrastructure
Prime Intellect provides an open superintelligence stack for training, evaluating, deploying, and continuously improving AI agents and models. Its platform combines RL environments, hosted training, inference, GPU compute, secure sandboxes, and open-source research tooling for researchers, startups, and enterprises.
Maintainer signals as of 8/23/2026
Funding history
Projects
About Prime Intellect, Inc.
Prime Intellect operates an integrated AI infrastructure platform spanning Lab, hosted reinforcement-learning training, evaluations, environments, inference, secure sandboxes, and on-demand or reserved GPU compute. It also develops open-source tools including Verifiers, prime-rl, and Prime Agent, supporting workflows from environment creation and model evaluation through post-training and production deployment. The company serves researchers, startups, enterprises, and teams building agentic AI systems, with customer examples including Ramp and Zapier.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Design, deploy, optimize, and support large-scale GPU infrastructure for customers, including GPU clusters, orchestration, high-performance networking, parallel filesystems, system performance, infrastructure troubleshooting, documentation, and operational support.
Requirements
- 3+ years of hands-on experience with GPU clusters and HPC environments
- Deep expertise with SLURM and Kubernetes in production GPU settings
- Experience with InfiniBand configuration and troubleshooting
- Strong understanding of NVIDIA GPU architecture, CUDA, and drivers
- Experience with Ansible and Terraform
- Proficiency in Python, Bash, and systems programming
- Customer-facing technical leadership experience
- Experience with NVIDIA drivers, Fabric Manager, and DCGM
- Experience configuring Docker, Containerd, and Enroot for GPUs
- Linux kernel tuning and performance optimization
- AI workload network topology design
- Knowledge of power and cooling requirements for high-density GPU deployments
Responsibilities
- Partner with clients to understand workload requirements and design GPU cluster architectures
- Create technical proposals and capacity plans 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 SLURM and Kubernetes
- Implement InfiniBand, RoCE, and NVLink networking
- Optimize GPU utilization, memory management, and inter-node communication
- Configure Lustre, BeeGFS, and GPFS filesystems
- Tune kernel and CUDA configurations
- Resolve customer infrastructure issues
- Implement monitoring, alerting, and automated remediation
- Provide 24/7 on-call support for critical customer deployments
- Create runbooks and documentation
Benefits
- Equity incentives