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. We have some of the most forward-thinking and talented people in the world working for us and, due to unprecedented growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with real passion for technology, we want to hear from you! Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
- Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
- 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 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
