About the Role
NVIDIA is looking for a Senior Validation Engineer in the DGX Server Product Engineering Team. In this role you will be working with a team of HW/SW engineers to develop and implement complex automated test plans for our industry leading GPU accelerated computing products.
What you will be doing:
•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.
What we need to see:
•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.
Ways to stand out from the crowd:
•Experience with NVIDIA Dynamo, TensorRT-LLM, vLLM, SGLang, or NCCL.
•CUDA kernel development or optimization experience using cuBLAS, CUTLASS, or related libraries.
•Familiarity with x86/Arm servers, NVLink, PCIe, InfiniBand/Ethernet, and distributed GPU systems.
•Experience using hardware error, power, thermal, and reliability telemetry to develop targeted stress coverage and isolate failures.
•A track record of building automation and turning experimental findings into measurable improvements in performance, coverage, or product quality.
Compensation
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 212,750 USD for Level 3, and 168,000 USD - 258,750 USD for Level 4. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 1, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.
