Description

What you will be doing:

Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters.

Study the interaction of our libraries with all HW (GPU, CPU, Networking) and SW components in the stack

Evaluate proof-of-concepts, conduct trade-off analysis when multiple solutions are available

Triage and root-cause performance issues reported by our customers

Collect a lot of performance data; build tools and infrastructure to visualize and analyze the information

Collaborate with a very dynamic team across multiple time zones

What we need to see:

M.S. (or equivalent experience) or PhD in Computer Science, or related field with relevant performance engineering and HPC experience

3+ yrs of experience with parallel programming and at least one communication runtime (MPI, NCCL, UCX, NVSHMEM)

Experience conducting performance benchmarking and triage on large scale HPC clusters

Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals)

Implement micro-benchmarks in C/C++, read and modify the code base when required

Ability to debug performance issues across the entire HW/SW stack. Proficient in a scripting language, preferably Python

Familiar with containers, cloud provisioning and scheduling tools (Kubernetes, SLURM, Ansible, Docker)

Adaptability and passion to learn new areas and tools. Flexibility to work and communicate effectively across different teams and timezones

Ways to stand out from the crowd:

Practical experience with Infiniband/Ethernet networks in areas like RDMA, topologies, congestion control

Experience debugging network issues in large scale deployments

Familiarity with CUDA programming and/or GPUs

Experience with Deep Learning Frameworks such PyTorch, TensorFlow

Key Skills

Education

Any Graduate