Used Tools & Technologies
Machine LearningRequired 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.
Linux @ 3
Python @ 3
MLOps @ 4
Communication @ 7
Networking @ 7
Debugging @ 4
Reporting @ 4
QA @ 4
Deep Learning @ 4
AI @ 4
- 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
NVIDIA is seeking a Senior Systems Software test (lead) Engineer to join our Cloud Service Provider (CSP) Engagements team, focusing on ML software stack validation for datacenter products such as GB200 and Vera Rubin. This role combines deep technical expertise from cluster to rack scale full-stack validation with customer-facing responsibilities, enabling cloud service providers with next-generation high-performance training and inference platforms. You will work at the intersection of hardware and software, validating stable and performant technical solutions from concept through deployment.
Responsibilities
- Define test strategy and test validation plans for CSP integration milestones; partner with hyperscalers to understand their test methodology, identify gaps, and provide NVIDIA recommendations.
- Reproduce, characterize, and triage customer bugs in the customer environment. Review internal test plans and test results, and publish summary test reports for each release for the rack-scale product during NPI phases.
- Validate fixes, mitigations, and release updates against deployed CSP software modules and known-good partner configurations.
- Partner with NVIDIA development teams to drive root-cause analysis and confirm release readiness with clear pass/fail evidence.
- 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.
- Manage large datasets of testing output and develop tooling for efficient retrieval of debug data, visualization, and reporting.
- Work with customers to localize problems using targeted reproduction steps by enabling stress and edge-case testing to assist development teams.
- Run performance benchmarks for both training and inference. Collaborate with AE, FAE, and Solution Architect teams on validation for customer issues and technical documentation. Replicate reported problems in the local lab.
Requirements
- 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.
- BS or MS in Computer Engineering, Computer Science, or related field (or equivalent experience).
- 8+ years of system software validation experience.
Ways to Stand Out
- Hands-on experience in cloud and cluster-level deployment and MLOps.
- Experience running deep learning workloads and related automation.
Compensation and Other Information
- Base salary ranges by level: Level 4: 184,000 USD - 287,500 USD; Level 5: 224,000 USD - 356,500 USD.
- Eligible for equity and benefits.
- Applications for this job will be accepted at least until June 29, 2026.
- NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer committed to fostering an inclusive work environment.