Job description
Act as the DRI for supercomputing clusters and GPU compute and interconnect fabric operations, ensuring GPU availability, service reliability, and AI training stability. Lead incident triage, mitigation, recovery, and root cause analysis for compute and fabric-related production issues across large-scale AI infrastructure. Perform deep, cross-stack debugging spanning hardware provisioning, GPU interconnect fabric, PCIe subsystems, and GPU interactions to identify and resolve complex failures. Drive operational excellence by identifying systemic failure patterns and developing technical guidance, troubleshooting procedures, playbooks, and escalation frameworks. Design and leverage automation, telemetry, and diagnostic tooling to improve issue detection, observability, debuggability, and mean time to mitigation (MTTM). Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 4+ years of experience operating high performance computing (HPC), artificial intelligence (AI), or largescale distributed systems in production environments 1+ years experience operating interconnect fabrics for HPC, AI, or largescale distributed systems in production Linux systems knowledge with demonstrated experience debugging low level infrastructure issues Demonstrated ability to reason across hardware, firmware, drivers, and software stacks to diagnose and resolve production issues