System Engineer, GPU Fleet

Fluidstack•Austin, TX

62d•$200,000 - $300,000

About The Position

As a System Engineer, GPU Fleet, 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.

Requirements

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

Nice To Haves

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 management for server hardware
Experience with high-performance networking (InfiniBand, RoCE, RDMA) and network troubleshooting in GPU cluster environments
Familiarity with datacenter operations including rack installations, cabling, power management, and thermal considerations

Responsibilities

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