Lead Systems Software Test Engineer – Csp Engagements

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Lead Systems Software Test Engineer for NVIDIA's Cloud Service Provider (CSP) Engagements team, focusing on validating the ML software stack for Datacenter products (e.g., GB200, Vera Rubin). The role involves full-stack validation from cluster to rack scale, customer bug reproduction and triage, defining test strategies, and collaborating with hardware/software development teams and hyper-scalers to ensure stable and performant training and inference platforms. Requires strong debugging, automation (Python), and communication skills, with experience in server platforms, OS, networking, and large-scale clusters. Experience with ML Ops and deep learning workloads is a plus.

What you'd actually do

  1. Define test strategy and test validation plans for CSP integration milestones, partner with hyper scalars to understand their test methodology, identify gaps and provide NVIDIA recommendations.
  2. Reproduce, characterize, and triage customer bugs in the customer environment. Review internal test plans, test results, and publish summary test report for each release for the rack scale product during NPI phases.
  3. Validate fixes, mitigations, and release updates against deployed CSP software modules and known-good partner configurations.
  4. Partner with NVIDIA development teams to drive root-cause analysis and confirm release readiness with clear pass/fail evidence
  5. Collaborate with CSP teams on provisioning, access, break-fix workflows, and environment readiness. Produce concise release-readiness summaries for internal stakeholders and partner-facing engineering reviews.

Skills

Required

  • Experience in validation, QA, system test, diagnostics, platform bring-up, or release qualification for complex hardware-software systems.
  • Strong understanding of server platforms, firmware, drivers, OS integration, networking, and large-scale cluster environments.
  • Hands-on experience debugging issues across hardware, firmware, software, networking, and infrastructure layers.
  • Ability to analyze logs, telemetry, diagnostic outputs, automation failures, and system health signals.
  • Familiarity with Linux environments, shell scripting, Python or similar automation, and CI/regression workflows. Experience creating test plans, regression suites, validation reports, and defect documentation.
  • Strong cross-functional communication skills with QA, development, field, support, and customer engineering teams.
  • Proficient in Python with strong background in test automation and test infrastructure design. Able to communicate effectively and collaborate with partner and customer teams.
  • 8+ years of system software validation experience.

Nice to have

  • Hands-on experience in cloud and cluster-level deployment and ML Ops.
  • Experience in running deep learning workloads and related automation

What the JD emphasized

  • ML software stack validation
  • high-performance training and inference platforms
  • customer bugs
  • deep learning workloads

Other signals

  • ML software stack validation
  • high-performance training and inference platforms
  • customer-facing responsibilities
  • deep technical expertise from cluster to rack scale full-stack validation