Senior CUDA Driver & Kernel Scheduler Engineer (Equity)

Nvidia Corporation in; Santa Clara (CA); On-site; USD 152,000 - 242,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a Senior Software Engineer for CUDA UMD - GPU Kernel Scheduling (Finance) to advance the CUDA Driver and its scheduling capabilities. You will collaborate across teams to design and implement features, shaping the future of CUDA APIs and programming models for diverse workloads.

The role emphasizes strong C/C++ skills, multithreading, and experience with large codebases in a Linux environment, with opportunities to impact AI, deep learning, and graphics workloads.

Qualifications

• BS or MS in Computer Science, Electrical Engineering or related field (or equivalent experience)
• 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 OS interfaces for threads, process control, and virtual memory
• Understanding of system level architecture including memory hierarchy and memory-mapped IO
• Experience writing and debugging multithreaded programs
• Good written communication and presentation skills

Responsibilities

• 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 important CUDA programming models and functionality such as CUDA Graphs
• Explore ways to use Graphs to improve scheduling of AI/ML workloads on GPUs
• Write effective, maintainable, and well-tested code
• Develop code for multiple operating systems

Skills

C/C++ programming

CUDA

Multithreading

Education

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

Tools

Linux Systems Software

Job description

NVIDIA is seeking a Senior Software Engineer for CUDA UMD - GPU Kernel Scheduling (Finance) to advance the CUDA Driver and its scheduling capabilities. You will collaborate across teams to design and implement features, shaping the future of CUDA APIs and programming models for diverse workloads.

The role emphasizes strong C/C++ skills, multithreading, and experience with large codebases in a Linux environment, with opportunities to impact AI, deep learning, and graphics workloads.