Come work for the team that brought to you NCCL, NVSHMEM & GPUDirect. Our GPU communication libraries are crucial for scaling Deep Learning and HPC applications! We are looking for a motivated Partner Enablement Engineer to guide our key partners and customers with NCCL. Most DL/HPC applications run on large clusters with high-speed networking (Infiniband, RoCE, Ethernet). This is an outstanding opportunity to get an end to end understanding of the AI networking stack. Are you ready for to contribute to the development of innovative technologies and help realize our vision?
What you will be doing:
Engage with our partners and customers to root cause functional and performance issues reported with NCCL.
Conduct performance characterization and analysis of NCCL and DL applications on groundbreaking GPU clusters.
Develop tools and automation to isolate issues on new systems and platforms, including cloud platforms (Azure, AWS, GCP, etc.).
Guide our customers and support teams on HPC knowledge and standard methodologies for running applications on multi-node clusters.
Document and conduct trainings/webinars for NCCL.
Engage with internal teams in different time zones on networking, GPUs, storage, infrastructure and support.
What we need to see:
B.S./M.S. degree in CS/CE or equivalent experience with 5+ years of relevant experience. Experience with parallel programming and at least one communication runtime (MPI, NCCL, UCX, NVSHMEM).
Excellent C/C++ programming skills, including debugging, profiling, code optimization, performance analysis, and test design.
Experience working with engineering or academic research community supporting HPC or AI.
Practical experience with high performance networking: Infiniband/RoCE/Ethernet networks, RDMA, topologies, congestion control.
Expert in Linux fundamentals and a scripting language, preferably Python.
Familiar with containers, cloud provisioning and scheduling tools (Docker, Docker Swarm, Kubernetes, SLURM, Ansible).
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:
Experience conducting performance benchmarking and developing infrastructure on HPC clusters. Prior system administration experience, esp for large clusters. Experience debugging network configuration issues in large scale deployments.
Familiarity with CUDA programming and/or GPUs. Good understanding of Machine Learning concepts and experience with Deep Learning Frameworks such PyTorch, TensorFlow.
Deep understanding of technology and passionate about what you do.
