Job Opportunities API

ABOUT FLUIDSTACK We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! ABOUT THE ROLE As a System Engineer, you will manage, operate, and optimize hyperscale GPU compute infrastructure supporting AI/ML training and inference workloads. Ensure high availability, performance, and reliability of GPU server fleet through automation, monitoring, troubleshooting, and collaboration with hardware engineering, platform teams, and datacenter operations. FOCUS - Operate and maintain large-scale GPU server fleet (H100, B200, GB200) supporting AI/ML workloads; monitor system health, performance, and utilization to maximize uptime and ensure SLA compliance - Perform hands-on troubleshooting and root cause analysis of complex hardware, firmware, OS, and application issues across GPU clusters; coordinate with vendors and hardware teams to resolve systemic failures - Develop and maintain automation scripts for provisioning, configuration management, monitoring, and remediation at scale. - Build and improve tooling for GPU health checks, performance diagnostics, driver validation, and automated recovery - Execute server provisioning, configuration, firmware updates, and OS installation using automation frameworks; manage lifecycle operations including deployment, maintenance, and decommissioning - Participate in 24x7 on-call rotation; respond to production incidents and coordinate resolution with cross-functional teams including datacenter operations, network engineering, and application teams - Lead post-incident reviews, document root causes, and drive continuous improvement initiatives focused on automation, reliability, monitoring, and operational efficiency BASIC QUALIFICATIONS - Bachelor's degree in Computer Science, Engineering, or related technical field (or equivalent practical experience) - 3+ years (System Engineer) or 5+ years (Senior System Engineer) in Linux system administration, datacenter operations, or infrastructure engineering - Strong Linux/Unix fundamentals including system administration, shell scripting (Bash, Python), troubleshooting, and performance tuning - Experience with server hardware architecture, troubleshooting techniques, and understanding of compute, memory, storage, and networking components - Experience in automation and configuration management tools (Ansible, Puppet, Chef, Terraform). - Strong analytical and problem-solving skills with ability to diagnose complex technical issues under pressure - Excellent communication and collaboration skills; ability to work effectively with cross-functional teams PREFERRED QUALIFICATIONS - Experience managing large-scale GPU infrastructure (NVIDIA H100, A100, B200, GB200) in production environments supporting AI/ML workloads - Deep knowledge of GPU architecture, CUDA toolkit, GPU drivers, monitoring tools (nvidia-smi, DCGM) - Experience with HPC cluster management, job schedulers (Slurm, PBS, LSF), and container orchestration (Kubernetes, Docker) - Proficiency in out-of-band management protocols (IPMI, Redfish, BMC) and firmware