System Software Engineer, Performance

About the Role

The AI revolution is powered by fast, efficient, and economical computation. At NVIDIA, performance is central to architectural invention becoming useful computing. CUDA is a critical layer where this transformation occurs, underpinning frameworks, libraries, and applications across AI, deep learning, HPC, graphics, automotive, robotics, and other CUDA-powered products. This team has unusual leverage to improve performance by reducing overhead in fundamental areas like launch, synchronization, memory, or data movement, or by creating new driver or runtime capabilities. A well-designed systems feature can help customers maximize their GPU utilization and inform future CUDA capabilities and GPU architectures. We are seeking systems software engineers to work at this leverage point.

What You'll Be Doing

• Design, implement, validate, and ship performance-centric features and programming-model capabilities in the CUDA driver and runtime, writing maintainable, well-tested production C/C++.
• Optimize critical execution paths—including kernel launch, synchronization, memory management & movement, CPU–GPU coordination, and system interconnect use—for latency, throughput, bandwidth, efficiency, and scalability.
• Own complex performance problems end-to-end: understand important workloads, form hypotheses, create focused measurements and models, isolate root causes across software and hardware boundaries, implement production solutions, and validate application-level impact.
• Establish performance expectations for current and future platforms, characterize new silicon, close software and hardware gaps, and drive performance readiness through product release.
• Translate workload and platform evidence into CUDA API and programming-model improvements, systems-software direction, and measurement-backed recommendations for future hardware architecture and implementation.
• Partner with application, library, framework, operating-system, driver, runtime, firmware, GPU architecture, silicon, product, and customer-facing teams; communicate findings clearly and raise engineering quality through design and code reviews.
• Lead complex feature development and cross-layer investigations across teams, define performance requirements and technical direction for major subsystems, mentor engineers, and shape hardware/software decisions for future product generations.

Requirements & Tech Stack

• Strong production C/C++ systems-programming experience, including delivery of substantial features, optimizations, or production fixes in a complex codebase.
• Strong operating systems and concurrency foundations, including threads, synchronization, processes, virtual memory, and user/kernel interactions.
• Strong computer-architecture foundations, including processors, memory hierarchy, caching and coherence, data movement, and system interconnects.
• Demonstrated success improving real software performance: measuring behavior, identifying the limiting mechanism, implementing an effective solution, and validating the result quantitatively.
• Sound technical judgment, ownership of ambiguous problems, and clear communication across organizational and disciplinary boundaries.
• Direct CUDA or GPU experience is valuable but not required when accompanied by deep systems-software, operating-systems, computer-architecture, and performance-engineering foundations.
• Python or another scripting language used for focused experimentation, data analysis, or visualization.

Qualifications

• A BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field—or equivalent practical experience — with at least 2 years of relevant systems-software development experience.

Ways To Stand Out From The Crowd

• Experience developing GPU or accelerator drivers, runtimes, kernel software, firmware, compilers, or other performance-critical low-level systems.
• Experience with pre-silicon analysis, platform bring-up, performance modeling, or hardware/software co-design.
• Systems-level performance experience with AI/DL, HPC, graphics, automotive, robotics, or similarly demanding workloads.
• Evidence of technical invention(s), such as software-performance patents, novel production designs, or measurement-backed recommendations that influenced a hardware revision or future architecture.