Senior System Software Engineer - AI Performance and Efficiency Tools

A key part of NVIDIA's strength is our sophisticated analysis / debugging tools that empower NVIDIA engineers to improve perf and power efficiency of our products and the running applications. We are looking for forward-thinking, hard-working, and creative people to join a multifaceted software team with high standards! This software engineering role involves developing tools for AI researchers and SW/HW teams running AI workload in GPU cluster.

As a member of the software development team, we will work with users from different departments like Architecture teams, Software teams. Our work brings the users intuitive, rich and accurate insight in the workload and the system, and empower them to find opportunities in software and hardware, build high level models to propose and deliver the best hardware and software to our customers, or debugging tricky failures and issues to help improve the performance and efficiency of the system.

What you’ll be doing:

• Build internal profiling and analysis tools for AI workloads at large scale

• Build debugging tools for common encountered problems like memory or networking

• Create benchmarking and simulation technologies for AI system or GPU cluster

• Partner with HW architects to propose new features or improve existing features with real world use cases

What we need to see:

• BS+ in Computer Science or related (or equivalent experience) and 5+ years of software development

• Strong software skills in design, coding (C++ and Python), analytical, and debugging

• Good understanding of Deep Learning frameworks like PyTorch and TensorFlow, distributed training and inference.

• Knowledge of GPU cluster job scheduling (Slurm or Kubernetes), storage and networking

• Experience with NVIDIA GPUs, CUDA Programming and NCCL

• Motivated self-starter with strong problem-solving skills and customer-facing communication skills

• Passion for continuous learning. Ability to work concurrently with multiple global groups

Ways to stand out from the crowd:

• Proven experience in GPU cluster scale continuous profiling & analysis tools/platforms

• Solid experience in large AI job performance analysis for training/inference workload

• Knowledge of Linux device drivers and/or compiler implementation

• Knowledge of GPU and/or CPU architecture and general computer architecture principles

#LI-Hybrid