Senior CUDA Kernel Scheduler & Driver Engineer

NVIDIA; Santa Clara (CA); Vor Ort; USD 152.000 - 288.000; Vollzeit

Benefits dieser Stelle

Equity

Benefits

Zusammenfassung

NVIDIA is seeking a seasoned System Software Engineer to work on the CUDA Driver, a core component for accelerating general purpose computation on the GPU. You will be part of a team delivering features and improvements to realize NVIDIA hardware for diverse workloads including deep learning, scientific computing, data science, and gaming.

Responsibilities include architecting and implementing new features, coordinating across teams, and shaping the CUDA APIs and models.

Qualifikationen

• Strong C and C++ programming skills.
• Minimum of 4 years of related development experience.
• Experience driving projects across multiple teams.
• Experience working with large codebases.
• Background with operating system interfaces for threads, process control, and virtual memory.
• Understanding of system level architecture (interconnects, memory hierarchy, interrupts, memory-mapped IO).
• Experience writing and debugging multithreaded programs.
• Good written communication and presentation skills.

Aufgaben

• Evangelize, architect, and implement new features.
• Coordinate and drive development efforts across multiple teams.
• Help define forward-looking improvements to the CUDA APIs and programming model.
• Extend CUDA programming models and functionality such as CUDA Graphs.
• Explore Graphs to improve scheduling of AI/ML workloads on GPUs.
• Write effective, maintainable, and well-tested code.
• Develop code for multiple operating systems.

Kenntnisse

C/C++ programming

Multithreaded programming

Team coordination

Large codebases

Ausbildung

BS or MS in Computer Science/EE or equivalent

Jobbeschreibung

NVIDIA is seeking a seasoned System Software Engineer to work on the CUDA Driver, a core component for accelerating general purpose computation on the GPU. You will be part of a team delivering features and improvements to realize NVIDIA hardware for diverse workloads including deep learning, scientific computing, data science, and gaming.

Responsibilities include architecting and implementing new features, coordinating across teams, and shaping the CUDA APIs and models.