NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can take on, and that matter to the world. This is our life’s work, to amplify human imagination and intelligence. Join us today!
As a member of the GPU/HPC Infrastructure team, you will provide leadership in the design and implementation of groundbreaking GPU compute clusters that run demanding deep learning, high performance computing, and computationally intensive workloads. We seek a technology leader to identify architectural changes and/or completely new approaches for improving HPC schedulers for serving many simultaneous and large multi-node GPU workloads with many complex dependencies. This role offers you an excellent opportunity to deliver production grade solutions, get hands on with ground-breaking technology, and work closely with technical leaders solving some of the biggest challenges in machine learning, cloud computing, and system co-design.
What you'll be doing:
Design and develop enhancements to the HPC batch scheduler(s).
Work extensively with HPC scheduler vendor on bug fixes and feature releases
Provide support to staff and end users to resolve batch scheduler issues
Build and improve our ecosystem around GPU-accelerated computing
Performance analysis and optimizations of deep learning workflows
Develop large scale automation solutions
Root cause analysis and suggest corrective action for problems large and small scales
Finding and fixing problems before they occur
What we need to see:
Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience with 5+ years of work experience
Strong understanding of HPC batch schedulers, such as Slurm, RTDA or LSF and HPC workflows that use MPI
Significant experience in Programming in C/C++ and advanced scripting in languages such as Python, Go, bash scripting
Established experience in Linux operating system, environment and tools
Accomplished in computer architecture and operating systems
Deep knowledge of Networking Protocols like InfiniBand, Ethernet
Experience analyzing and tuning performance for a variety of HPC workloads
In-depth understating of container technologies like Docker, Singularity, Podman
Flexibility/adaptability for working in a dynamic environment with different frameworks and requirements
Excellent communication, interpersonal and customer collaboration skills
Ways to stand out from the crowd:
Knowledge in MPI and High-performance computing
Background in RDMA technology
Experience in kernel programming
Open Source Software Contributor
Experience with deep learning frameworks like PyTorch and TensorFlow
Passionate about SW development processes
Want to make what was impossible possible!
You will also be eligible for equity and . NVIDIA accepts applications on an ongoing basis.
Senior GPU Supercomputer Scheduler Engineer • Westford, MA, US
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