Software Engineering Manager - GPU Communications Libraries
$184,000–$287,500 year
On-siteSanta Clara, California, United States
EXPIREDSanta Clara, California, United StatesOn-siteFull Time$184,000–$287,500 yearDoctorate Or Professional DegreeEnterprise
Full TimeDoctorate Or Professional DegreeEnterprise
Job Summary
Lead, mentor, and grow the library engineering team while planning and executing projects for NVSHMEM and UCX. Participate in feature design and implementation, and interact with internal and external partners to define the product roadmap. Continuously review and identify improvement opportunities in established processes, infrastructure, and practices to ensure efficient team execution. Manage high-performance communication libraries for Deep Learning and HPC applications across tens of thousands of GPUs.
Required Qualifications
- 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 performance analysis/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
- Experience with parallel programming models (MPI, SHMEM)
- Experience with 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.
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