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 GPU Cluster users and admins.
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 stability of the system.
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What you'll be doing:
- Build internal perf/power profiling and analysis tools and platform for AI workloads at cluster scale
- Build debugging tools for common encountered problems in GPU cluster
- Work with our users to build / calibrate perf/power models for next generation HW or system
- Partner with architects to propose new HW 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 design and implementation ability with Python/Go/C++
- Good understanding of Deep Learning and AI frameworks like Pytorch, TensorFlow and etc
- Knowledge of AI cluster job scheduling, storage management and networking management
- Knowledge of Linux kernel
- Excellent problem solving skills and project management skills
- Flexibility for working in an evolving environment with changing requirements
Ways to stand out from the crowd:
- Proven experience in GPU cluster scale continuous profiling & analysis tools/platforms
- Solid experience in large AI job troubleshooting and failure detection/recovery
- Skillful in Deep Learning application performance analysis and optimization
- Knowledgeable in GPU / CPU architecture and application performance or power efficiency analysis