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 inters [... source excerpt omitted ...] 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 inclu [... source excerpt omitted ...] 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 an