Senior GPU Infrastructure Architect for Frontier AI

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.

Core Technical Responsibilities

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

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

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

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.

Cash Compensation Range of $150-300k plus Equity Incentives #J-18808-Ljbffr