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Nvidia Software Engineering Manager - GPU Communications Libraries 
United States, California 
944178863

24.06.2024

What you will be doing:

  • Lead, mentor, and grow your library engineering team and be responsible for the planning and execution of projects as well as the quality, and performance of your libraries.

  • This is a technical leadership role so you will participate in feature design and implementation.

  • Interact with internal and external partners and researchers to understand their use cases and requirements. Collaborate with engineering teams, program and product management, and partners to define the product roadmap.

  • Continuously review and identify improvement opportunities in established processes, infrastructure, and practices to ensure the teams are executing in the most efficient and transparent manner.

What we need to see:

  • 10+ overall years of experience in the software industry with specialization in HPC networking or system software.

  • 4+ years of management experience.

  • BS, MS, or Ph.D. in CS, CE, EE (related technical field) or equivalent experience.

  • Prior systems software or communication runtime or high performance networking software development experience with a successful track record of taking several complex software features or products through the full product life cycle.

  • Strong understanding of computer system architecture, operating systems principles (aka systems software fundamentals), HW-SW interactions and performanceanalysis/optimizations.

  • Excellent C/C++ programming and debugging skills in Linux.

  • Experience balancing multiple projects with competing priorities.

  • Flexibility to work and communicate effectively across different teams and timezones.

Ways to stand out from the crowd:

  • Experience with parallel programming models (MPI, SHMEM) and at least one communication runtime (MPI, NCCL, NVSHMEM, OpenSHMEM, UCX, UCC). Experience with programming using CUDA, MPI, OpenMP, OpenACC, pthreads.

  • Background with RDMA, high-performance networking technologies (InfiniBand, RoCE, Ethernet, EFA), network architecture and network topologies. Knowledge of HPC and ML/DL fundamentals.

  • Experience with Deep Learning Frameworks such PyTorch, TensorFlow, etc.

You will also be eligible for equity and .