Nvidia GPU Computing Capacity Optimization Engineer 

United States, Texas 

145651574

As a member of the GPU AI/HPC Infrastructure team, you will provide leadership in the design and implementation ofground breakingGPU compute clusters that run demanding deep learning, high performance computing, and computationally intensive workloads. In this role we seek an expert tothe Capacity management and allocation in GPU Compute Clusters. You will help us with the strategic challenges wein maximizing andour usage of all datacenter resources including, storage,and power. You will help build methodologies,and metrics to enable effective resourcein a heterogeneousenvironment, andwith growth planning across our global computing environment.

be doing:

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

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

• Diagnosing customerutilizationdeficiencies and job scheduling issues

• Building automation,toolsand metrics to help us increase productiveutilizationofresources

• Collaborating with the scheduler team to improve schedulingalgorithms

• Root cause analysis and suggest corrective action for problems large and smallscales

• Finding and fixing problems before they

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 andoperatinglarge scalecomputeinfrastructure.

• 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 asSlurm, K8s, RTDA or LSF

• Familiarity with container technologies like Docker, Singularity, Shifter,Charliecloud

• Proficient in Python programming and bash scripting

• Experience with AI/HPC workflows that use MPI

Ways to stand out from the crowd:

• MLPerfbenchmarking

• Experience with Machine Learning and Deep Learning concepts,algorithmsandmodels

• Proficient in Centos/RHEL and/or Ubuntu Linux distros

• Familiarity with InfiniBand with IBOP and RDMAas well as understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads

• Familiarity with deep learning frameworks likePyTorchand TensorFlow

a creative and autonomous engineer with