Senior Systems Performance Engineer
$136k - $259k • Remote • US, CA, Santa Clara
Posted 1d ago
About the job
NVIDIA is seeking a Senior Validation Engineer to join the DGX Server Product Engineering Team. In this role, you will collaborate with a team of hardware and software engineers to develop and implement sophisticated automated test plans for our leading GPU-accelerated computing products. You will be instrumental in ensuring the performance and reliability of cutting-edge AI hardware by developing and tuning workloads, adapting state-of-the-art AI training and inference frameworks, and applying architectural analysis to characterize workload behavior and identify bottlenecks. This position offers the opportunity to make a significant impact on the future of computing by working on AI-driven technologies.
Responsibilities
- Develop and tune synthetic GPU workloads using CUDA or GPU programming DSLs, and application workload recipes using AI training and inference frameworks, to stress targeted hardware subsystems and expand validation coverage.
- Onboard and adapt state-of-the-art AI training and inference workloads to new GPU platforms.
- Apply architectural analysis and develop system-level performance models to characterize workload behavior, identify bottlenecks, and guide workload selection and stress coverage.
- Design controlled experiments and correlate model predictions with workload profiles, system telemetry, and measured results; investigate differences and refine workloads, models, and test strategies.
- Analyze performance anomalies, workload correctness, and hardware and software failures; create reproducible cases and partner with architecture, design, and software teams to drive fixes and verify improvements.
- Build automation for workload deployment, experiment execution, telemetry collection, and analysis across systems and configurations.
Requirements
- BS in Computer Engineering, Electrical Engineering, Computer Science, or a related field, or equivalent experience.
- 5+ years of relevant experience in systems performance engineering, performance modeling, accelerated computing, or complex system development and debug.
- Deep understanding of computer architecture, including GPU and CPU execution, memory hierarchies, interconnects, and their impact on application performance.
- Experience developing analytical or simulation-based performance models and validating predictions against measurements from running systems.
- Hands-on experience deploying, profiling, and tuning AI training or inference workloads using frameworks such as PyTorch, TensorRT-LLM, vLLM, or SGLang.
- Strong Python programming and data analysis skills, including designing controlled experiments, interpreting telemetry, and communicating findings.
- Working knowledge of CUDA and GPU profiling tools, with the ability to connect application behavior to hardware utilization and bottlenecks.
- Experience debugging workloads and system behavior in Linux environments, and collaborating across hardware and software teams.
Benefits
- Highly competitive salaries
- Comprehensive benefits package
- Equity