Principal Supercomputing Operations Software Engineer

Microsoft Microsoft · Big Tech · United States · Software Engineering

This role is for a Principal Supercomputing Operations Software Engineer within Microsoft Azure's AI/HPC organization. The primary focus is on operating, debugging, and scaling hyperscale GPU clusters, specifically managing interconnect fabrics (InfiniBand and GPU interconnects) that are critical for AI training performance and customer SLAs. The role involves leading incident response, defining operational strategies, and driving automation and telemetry to improve system reliability and debuggability for large-scale AI infrastructure.

What you'd actually do

  1. Serve as the technical authority and DRI for InfiniBand and GPU interconnect fabric operations across large scale AI supercomputing environments, ensuring sustained GPU availability, training stability, and SLA compliance
  2. Lead and orchestrate complex, high severity fabric incidents end to end, including detection, triage, mitigation, recovery, and root cause analysis, making high impact decisions under ambiguity
  3. Perform deep, multi layer systems debugging across InfiniBand, Subnet Manager, GPU interconnect, PCIe, GPUs, firmware, drivers, and OS layers to identify true root causes at fleet scale
  4. Drive operational excellence and systemic prevention by identifying recurring failure patterns, defining reliability models and failure domains, and authoring authoritative TSGs, playbooks, and escalation frameworks adopted across teams
  5. Architect and drive automation, telemetry, diagnostics, and tooling that materially improve detection, observability, debuggability, and mean time to mitigation, raising the operational bar for interconnect fabrics across the platform

Skills

Required

  • Bachelor'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
  • equivalent experience

Nice to have

  • operating large‑scale distributed systems, high‑performance computing (HPC), or artificial intelligence (AI) infrastructure in production environments
  • Demonstrated ownership of mission‑critical production infrastructure with direct impact on service availability, GPU workloads, and customer SLAs
  • Hands‑on experience operating and debugging interconnect fabrics supporting large‑scale compute workloads
  • Strong Linux systems knowledge with experience debugging low‑level infrastructure issues across operating systems, drivers, and services
  • Proven ability to reason across hardware, firmware, drivers, and software stacks to diagnose and resolve complex production issues
  • Master'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

What the JD emphasized

  • technical authority
  • strategic owner
  • reliability engineering
  • high stakes technical decisions
  • technical leadership
  • operational standards
  • DRI
  • high severity fabric incidents
  • multi layer systems debugging
  • recurring failure patterns
  • reliability models
  • failure domains
  • automation, telemetry, diagnostics, and tooling

Other signals

  • powers some of the world’s largest cloud native supercomputers used for frontier AI training
  • hyperscale GPU clusters
  • interconnect fabrics are a first order reliability system that directly determines GPU availability, training throughput, and customer SLAs
  • technical authority and strategic owner for interconnect fabric operations across flagship AI supercomputing environments
  • operating at the intersection of architecture, live operations, and reliability engineering
  • lead the most complex and impactful fabric related incidents
  • define failure models, operational strategy, and systemic prevention mechanisms
  • architect and drive automation, diagnostics, and telemetry that materially improve operability and debuggability of interconnect fabrics
  • Azure’s largest AI platforms scale safely, predictably, and sustainably to meet the demands of next generation AI workloads