Nvidia Software Engineering Manager - GPU Communications Libraries 

United States, California 

944178863

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.