Description
NVIDIA is seeking a Senior Validation Engineer in the DGX Server Product Engineering Team. You will work with a team of HW/SW engineers to develop and implement complex automated test plans for GPU accelerated computing products.
Responsibilities:
- Develop and tune synthetic GPU workloads using CUDA or GPU programming DSLs, and application workload recipes using AI training and inference frameworks.
- 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.
- Analyze performance anomalies, workload correctness, and hardware and software failures.
- 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.
- Working knowledge of CUDA and GPU profiling tools.
- Experience debugging workloads and system behavior in Linux environments, and collaborating across hardware and software teams.
Nice to Have:
- 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.
NVIDIA offers highly competitive salaries and a comprehensive benefits package.