Principal Software Engineer, At-Scale Reliability and Fleet Intelligence — CSP Engagements
Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Statistics @ 4
Leadership @ 7
Communication @ 7
Stress Testing @ 4
Reporting @ 4
GPU @ 4
Observability @ 4
AI @ 4
HPC @ 4
NVLink @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Were 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 NVIDIAs 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 NVIDIAs 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.
Responsibilities
- 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 NVIDIAs 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 NVIDIAs 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
Requirements
- 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
About NVIDIA
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 youre creative, hardworking and self-motivated, we want to hear from you!
Compensation & Benefits
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 (see NVIDIA benefits link in original posting).
Application
Applications for this job will be accepted at least until June 30, 2026.
NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.