Requirements:

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

Nice to Haves:

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

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

NVIDIA is at the forefront of breakthroughs in Artificial Intelligence, High-Performance Computing, and Visualization
Highly competitive salaries
Extensive benefits package
Work environment that promotes diversity, inclusion, and flexibility