Senior Systems Performance Engineer

NVIDIA; United States; On-site; USD 150,000 - 190,000; Full time

Job summary

NVIDIA Corporation in the United States is seeking a Senior Validation Engineer to join the DGX Server Product Engineering Team. You will collaborate with HW/SW engineers to develop and implement automated test plans for our GPU-accelerated computing products.

You will design synthetic GPU workloads with CUDA, onboard AI training and inference workloads to new platforms, build system-level performance models, run controlled experiments, analyze anomalies, and automate workload deployment and

Qualifications

• BS in Computer Engineering, Electrical Engineering, CS, or related field.
• 5+ years in systems performance engineering or related fields.
• Deep understanding of computer architecture (GPU/CPU, memory hierarchies).
• Experience developing analytical or simulation-based performance models.
• Hands-on experience deploying and tuning AI workloads with PyTorch, TensorRT-LLM, vLLM, or SGLang.
• Strong Python programming and data analysis skills.
• Knowledge of CUDA and GPU profiling tools.
• Experience debugging workloads in Linux and collaborating across teams.

Responsibilities

• Develop and tune synthetic GPU workloads using CUDA and AI workloads to stress hardware subsystems.
• Onboard and adapt AI training and inference workloads to new GPU platforms.
• Build system-level performance models to characterize workload behavior and bottlenecks.
• Design controlled experiments and compare model predictions with telemetry and results.
• Analyze performance anomalies and collaborate to drive fixes and verify improvements.
• Build automation for workload deployment, experiment execution, telemetry collection, and analysis.

Skills

Systems performance engineering

Performance modeling

Accelerated computing

Complex system development

Debugging

Python programming

CUDA programming

GPU profiling

Linux debugging

Data analysis

Education

BS in Computer Engineering, Electrical Engineering, Computer Science or related field

Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology-and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI - the next era of computing. NVIDIA is a "learning machine" that constantly evolves by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world. This is our life's work, to amplify human imagination and intelligence. Make the choice to join us today. We are now 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.

NVIDIA offers highly competitive salaries and a comprehensive benefits package.