Member of Technical Staff - GPU Infrastructure

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. We're hiring for multiple GPU Infrastructure positions: one based in the APAC region and one based in the USA. 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 architecturesCreate technical proposals and capacity planning for clusters ranging from 100 to 10,000+ GPUsDevelop deployment strategies for LLM training, inference, and HPC workloadsPresent architectural recommendations to technical and executive stakeholders Infrastructure Deployment & Optimization Deploy and configure orchestration systems including SLURM and Kubernetes for distributed workloadsImplement high-performance networking with InfiniBand, RoCE, and NVLink interconnectsOptimize GPU utilization, memory management, and inter-node communicationConfigure parallel filesystems (Lustre, BeeGFS, GPFS) for optimal I/O performanceTune system performance from kernel parameters to CUDA configurations Production Operations & Support Serve as primary technical escalation point for customer infrastructure issuesDiagnose and resolve complex problems across the full stack - hardware, drivers, networking, and softwareImplement monitoring, alerting, and automated remediation systemsProvide 24/7 on-call support for critical customer deploymentsCreate runbooks and documentation for customer operations teams Technical Requirements Required Experience 3+ years hands-on experience with GPU clusters and HPC environmentsDeep expertise with SLURM and Kubernetes in production GPU settingsProven experience with InfiniBand configuration and troubleshootingStrong understanding of NVIDIA GPU architecture, CUDA ecosystem, and driver stackExperience with infrastructure automation tools (Ansible, Terraform)Proficiency in Python, Bash, and systems programmingTrack 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 optimizationNetwork topology design for AI workloadsPower and cooling requirements for high-density GPU deployments Nice to Have Experience with 1000+ GPU deploymentsNVIDIA DGX, HGX, or SuperPOD certificationDistributed training frameworks (PyTorch FSDP, DeepSpeed, Megatron-LM)ML framework optimization and profilingExperience with AMD MI300 or Intel Gaudi acceleratorsContributions to open-source HPC/AI infrastructure projects What we offer Cash compensation of $150-300k with significant equity incentivesFlexible work arrangement (remote or San Francisco office)Full visa sponsorship and relocation supportA professional development budget for courses and conferencesRegular team off-sites and conference attendanceThe opportunity to shape core infrastructure for frontier AI research 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.