Job description

We're looking for a Principal Software Engineer to join our CSP Engagements team as the technical focal point for fleet-scale reliability, working directly with engineering teams of key CSP / hyperscale customers to ensure NVIDIA platforms achieve target MTBI (Mean Time Between Interruptions) in production. In this role, you will augment NVIDIA's internal software/firmware and quality teams with a dedicated CSP-facing focus. You will drive work streams with CSP engineering teams to build shared understanding of reliability software/firmware architecture, methodology, incorporate their fleet telemetry and failure data into NVIDIA's improvement priorities, and validate that reliability improvements measured in the lab translate to real customer environments. Your cross-CSP visibility enables you to distinguish systemic architectural gaps from environmental or configuration-specific issues that no single customer engagement could identify alone. What you'll be doing: • Drive reliability work streams with CSP engineering teams — ensuring shared understanding of MTBI measurement methodology, failure classification, and health monitoring architecture • Gather and synthesize CSP fleet reliability data — identify failure patterns that appear across multiple customers and champion improvements back into NVIDIA's firmware, driver, and hardware teams • Define consistent MTBI measurement methodology that works across different CSP monitoring environments and operational practices • Conduct fleet-scale failure pattern analysis using statistical methods (Pareto, survival analysis, Weibull) to classify failures as systemic, environmental, or configuration-specific • Drive fleet health monitoring integration architecture — ensure NVIDIA's health agents, telemetry, and reporting align with CSP operational workflows and automation • Define burn-in reliability test environment and cluster certification criteria in collaboration with quality teams, validating with customers that criteria are meaningful • Collaborate with CSPs to ensure reliability-related integration work (health monitoring deployment, telemetry pipeline, alerting configuration) is complete ahead of at-scale launch • Develop predictive failure models using fleet telemetry and validate their effectiveness in customer environments What we need to see: • 15+ years of experience in systems software at datacenter scale, or reliability engineering with focus on at-scale challenges. • BS or MS in Computer Science, Electrical Engineering, Statistics, or related field (or equivalent experience) • Deep expertise in multi-NUMA, rack-scale system software and firmware. Statistical failure analysis methods: MTBF/MTBI calculation, Pareto analysis, root cause classification • Experience with fleet-level telemetry and observability systems: time-series databases, anomaly detection, health scoring, event correlation • Understanding of hardware failure modes in large-scale GPU/accelerator deployments — ability to classify and prioritize across compute, interconnect, memory, power, and thermal domains • Experience defining or operating burn-in, stress testing, or certification frameworks for complex hardware systems. Familiarity with predictive maintenance or anomaly detection approaches applied to fleet health data • Customer obsession — genuine passion for understanding fleet reliability challenges at scale and translating them into actionable engineering priorities • Strong communication — ability to present statistical reliability findings to both deep technical audiences and executive leadership. Demonstrated success driving cross-functional improvements across hardware, firmware, and software teams without direct authority Ways to stand out from the crowd: • Experience in fleet reliability at a hyperscaler (hardware health, fleet reliability at leading CSP/Hyperscaler) • Familiarity with NVIDIA GPU error taxonomy (Xid errors, NVLink error counters, thermal events, CPER records) • Experience building health scoring or predictive failure models for accelerator or HPC infrastructure • Background in defining MTBI/MTBF measurement standards or certification programs for complex multi-component systems • Understanding of how reliability data flows from device firmware through telemetry pipelines to fleet-level dashboards and automated remediation NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. We have some of the most forward-thinking and hardworking people on the planet working for us. If you're creative, hardworking and self-motivated, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 28, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Drive reliability work streams with CSP engineering teams — ensuring shared understanding of MTBI measurement methodology, failure classification, and health monitoring architecture
Gather and synthesize CSP fleet reliability data — identify failure patterns that appear across multiple customers and champion improvements back into NVIDIA's firmware, driver, and hardware teams
Define consistent MTBI measurement methodology that works across different CSP monitoring environments and operational practices
Conduct fleet-scale failure pattern analysis using statistical methods (Pareto, survival analysis, Weibull) to classify failures as systemic, environmental, or configuration-specific
Drive fleet health monitoring integration architecture — ensure NVIDIA's health agents, telemetry, and reporting align with CSP operational workflows and automation
Define burn-in reliability test environment and cluster certification criteria in collaboration with quality teams, validating with customers that criteria are meaningful
Collaborate with CSPs to ensure reliability-related integration work (health monitoring deployment, telemetry pipeline, alerting configuration) is complete ahead of at-scale launch
Develop predictive failure models using fleet telemetry and validate their effectiveness in customer environments
15+ years of experience in systems software at datacenter scale, or reliability engineering with focus on at-scale challenges.
BS or MS in Computer Science, Electrical Engineering, Statistics, or related field (or equivalent experience)
Deep expertise in multi-NUMA, rack-scale system software and firmware. Statistical failure analysis methods: MTBF/MTBI calculation, Pareto analysis, root cause classification
Experience with fleet-level telemetry and observability systems: time-series databases, anomaly detection, health scoring, event correlation
Understanding of hardware failure modes in large-scale GPU/accelerator deployments — ability to classify and prioritize across compute, interconnect, memory, power, and thermal domains
Experience defining or operating burn-in, stress testing, or certification frameworks for complex hardware systems. Familiarity with predictive maintenance or anomaly detection approaches applied to fleet health data
Customer obsession — genuine passion for understanding fleet reliability challenges at scale and translating them into actionable engineering priorities
Experience in fleet reliability at a hyperscaler (hardware health, fleet reliability at leading CSP/Hyperscaler)
Familiarity with NVIDIA GPU error taxonomy (Xid errors, NVLink error counters, thermal events, CPER records)
Experience building health scoring or predictive failure models for accelerator or HPC infrastructure
Background in defining MTBI/MTBF measurement standards or certification programs for complex multi-component systems
Understanding of how reliability data flows from device firmware through telemetry pipelines to fleet-level dashboards and automated remediation
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until September 28, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

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Platform: eightfold
Effort: medium