GPU Fabric EngineerVultr is seeking a highly skilled and experienced
GPU Fabric Engineer to validate, troubleshoot, and optimize high-speed networking fabrics for GPU clusters powering large-scale AI training and inference workloads. The ideal candidate is deep hands-on experience with InfiniBand and RoCE fabrics in GPU cluster environments, and a strong understanding of how interconnect performance impacts distributed AI workloads. This is a highly visible role in a high-growth technology company, which will require expertise in fabric validation and tuning for AI workloads, the ability to diagnose complex multi-layer issues, and close collaboration with GPU engineering and networking teams. This is your opportunity to join our fast growing team and leave your mark on Vultr and the future of Cloud Infrastructure.
Key Responsibilities- Validate and troubleshoot InfiniBand and RoCE fabrics during GPU cluster bring-up and expansion
- Tune fabric performance parameters for distributed AI workloads (NCCL, MPI, collective operations)
- Monitor and manage fabrics using NVIDIA UFM (Unified Fabric Manager) for health, topology, and performance visibility
- Diagnose and resolve fabric-level issues including link errors, congestion, packet loss, and path asymmetry
- Optimize RDMA transport settings, PFC/ECN behavior, and lossless queue configuration for GPU traffic
- Validate fabric performance benchmarks and ensure line-rate throughput for AI workloads
- Collaborate with GPU Engineers to correlate fabric health with workload performance
- Collaborate with networking teams on fabric provisioning, configuration, and remediation
- Respond to fabric alerts and degradation events across production GPU clusters
- Document fabric troubleshooting procedures, tuning parameters, and validation runbooks
Qualifications- 3-7 years of experience in network engineering, HPC fabric, or GPU infrastructure
- Hands-on experience with InfiniBand and/or RoCE fabrics in GPU cluster environments
- Experience with NVIDIA UFM for fabric management, monitoring, and diagnostics
- Strong understanding of RDMA transport, lossless Ethernet design, and congestion management (PFC, ECN, DCQCN)
- Experience with GPU cluster networking and distributed communication libraries (NCCL, MPI)
- Familiarity with GPU platforms and their interconnect requirements (NVIDIA NVLink, NVSwitch, ConnectX)
- Experience with fabric diagnostic tools (ibstat, ibqueryerrors, perfquery, etc.)
- Proficiency in Python or Bash for scripting and validation
- Basic understanding of Linux systems and server hardware
- Strong troubleshooting and analytical skills across network and system layers
Compensation$125,000 - $135,000
Final compensation will vary depending on years of experience, background/skill set, location, and applicable laws.
