Hardware Health

About this role

Design and develop next-generation hardware health monitoring and diagnostic frameworks for large GPU clusters (NVL16/NVL72/GB200+ scale). Build predictive analytics pipelines leveraging telemetry, power, and thermal data to anticipate hardware degradation and systemic issues. Collaborate with silicon, firmware, and datacenter engineers to identify root causes and remediate large-scale hardware anomalies

Define system health KPIs (e.g., NIS/RIS, MTBF, failure domain analysis) and integrate them into real-time observability platforms. Lead incident triage for high-impact GPU, network, and cooling issues across distributed clusters. Drive automation in health management to reduce manual intervention to the top 5% of anomalies

Partner with cross-functional teams to influence hardware design for reliability, thermal efficiency, and serviceability. 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 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 OR Bachelor'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 equivalent experience. Experience working with large-scale HPC or GPU systems (NVIDIA H100/GB200 or equivalent)

Deep understanding of GPU architecture, high-speed interconnects (NVLink, InfiniBand, RoCE), and large datacenter topologies. Proficiency in hardware telemetry, diagnostics, or failure analysis tools. Experience with exascale-class systems or cloud-scale AI clusters

Familiarity with reliability modeling, machine learning-based anomaly detection, or predictive maintenance. Contributions to large-scal

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