Principal Software Engineer

Lead the architecture for near-real-time telemetry ingestion, processing, storage, and query across GPUs, hosts, InfiniBand, Ethernet, NVLink, storage, power, cooling, and related supercomputer components. Define shared signal contracts and measurable standards for freshness, completeness, correctness, availability, retention, access, and cost; validate the end-to-end pipeline under production-scale conditions. Connect telemetry to a common resource identity, topology, and health model so users can navigate from fleet and cluster health to racks, nodes, GPUs, switches, and links, and understand dependencies and blast radius. Drive convergence of 1P and 3P GPU capacity telemetry into a consistent operational model and experience, making provider-specific gaps and differences in signal quality explicit. Build the foundations for AI-assisted anomaly detection, cross-signal correlation, likely-cause analysis, impact assessment, and actionable recommendations; establish evaluation and feedback loops so insights are trustworthy. Partner with customers, datacenter technicians, service owners, and engineering DRIs to define persona-appropriate workflows and secure, role-based views of the same underlying state. Build practical tools and dashboards for datacenter technicians that surface near-real-time component health, likely fault location, repair context, and the next diagnostic step, so technicians can act quickly at the rack or node. Create a fast repair-validation loop: show technicians whether a repair changed the relevant signals, whether the component and affected capacity recovered, and what remains unhealthy so they can iterate until the problem is resolved. Lead technical design reviews, identify cross-team dependencies, establish reusable interfaces and patterns, and guide teams from proof of concept through incremental production rollout. Define success measures such as signal freshness, coverage, time to diagnose, time to repair, repair-verification latency, repeat repair attempts, recovery time, and the ability to connect infrastructure events to affected GPU capacity and customer impact. Mentor engineers and influence teams across organizational boundaries; contribute to operational playbooks and continuously improve the platform based on production learning. 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 Master's Degree in Computer Science or related technical field AND 12+ 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 15+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience architecting large-scale telemetry, streaming-data, or observability systems, including ingestion, processing, storage, query, real-time analytics, time-series data, topology or graph models, and fleet-scale visualization. Experience leading cross-team technical design and delivery of production services, with attention to reliability, security, and operational readiness. Experience with GPU infrastructure, AI training or inference systems, high-performance computing, or datacenter operations. Knowledge of InfiniBand, Ethernet, NVLink, host and accelerator telemetry, and the dependencies among compute, networking, storage, and facility systems. Experience applying machine learning or AI to operational data, including anomaly detection, correlation, root-cause assistance, and evaluation of insight quality. Experience integrating telemetry from multiple providers or environments while maintaining common identity, schema, access-control, and data-quality standards. Ability to translate complex infrastructure signals into clear customer impact and technician-ready repair workflows, with measurable improvements in diagnosis, repair validation, recovery, and GPU utilization.