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 HPC workloads.
Working knowledge of cluster configuration managements tools such as Ansible, Puppet, Salt.
Experience with HPC cluster job schedulers such as SLURM, 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 HPC workflows that use MPI
Ways to stand out from the crowd:
Understanding of MLPerf benchmarking
Familiarity with InfiniBand with IBOP and RDMA
Understanding of fast, distributed storage systems like Lustre and GPFS for HPC workloads.
Background with Software Defined Networking and HPC cluster networking
Familuarity with deep learning frameworks like PyTorch and TensorFlow