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 tackle, and that matter to the world. This is our life’s work, to amplify human imagination and intelligence. Make the choice to join us today!

As a member of the GPU AI/HPC Infrastructure team, you will provide leadership in the design and implementation of ground breaking GPU compute clusters that run demanding deep learning, high performance computing, and computationally intensive workloads. We seek an expert to identify architectural changes and/or completely new approaches for our GPU Compute Clusters. As an expert, you will help us with the strategic challenges we encounter including: compute, networking, and storage design for large scale, high performance workloads, effective resource utilization in a heterogeneous compute environment, evolving our private/public cloud strategy, capacity modeling, and growth planning across our global computing environment.

What you'll be doing:

Building and improving our ecosystem around GPU-accelerated computing including developing large scale automation solutions

Maintaining and building deep learning clusters at scale

Supporting our researchers to run their flows on our clusters including performance analysis and optimizations of deep learning workflows

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.

Minimum 5 years of experience designing and operating large scale compute infrastructure.

Experience analyzing and tuning performance for a variety of AI/HPC workloads.

Working knowledge of cluster configuration managements tools such as Ansible, Puppet, Salt.

Experience with AI/HPC advanced job schedulers, and ideally familiarity with schedulers such as Slurm, K8s, RTDA or LSF

In depth understating of container technologies like Docker, Singularity, Shifter, Charliecloud

Proficient in Centos/RHEL and/or Ubuntu Linux distros including Python programming and bash scripting

Experience with AI/HPC workflows that use MPI

Ways to stand out from the crowd:

Experience with NVIDIA GPUs, Cuda Programming, NCCL and MLPerf benchmarking

Experience with Machine Learning and Deep Learning concepts, algorithms and models

Familiarity with InfiniBand with IBOP and RDMA

Understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads

Familiarity with deep learning frameworks like PyTorch and TensorFlow

NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most brilliant and talented people in the world working for us and, due to unprecedented growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with real passion for technology, we want to hear from you.

#LI-Hybrid

The base salary range is 148,000 USD - 339,250 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

You will also be eligible for equity and benefits. NVIDIA accepts applications on an ongoing basis.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.