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# Senior Software Architect - Deep Learning and HPC Communications

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $184,000.0 - $287,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Computer Clusters, Nvidia CUDA, Computer Programming, Software Debugging, Linux, InfiniBand, Network Architecture, Tensorflow, Subsystems, Systems Architecture, Graphics Processing Unit (GPU), High Performance Computing, Pytorch, Deep Learning, Parallel Computation
- **Published:** August 28, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Software-Architect---Deep-Learning-and-HPC-Communications_JR2001099-1

## About the Role

* M.S./Ph.D. degree in CS/CE or equivalent experience.
 * 5+ years of relevant experience.
 * Excellent C/C++ programming and debugging skills.
 * Experience with parallel programming models (MPI, SHMEM) and at least one communication runtime (MPI, NCCL, NVSHMEM, OpenSHMEM, UCX, UCC).
 * Deep understanding of operating systems, computer and system architecture.
 * Solid in fundamentals of network architecture, topology, algorithms, and communication scaling relevant to AI and HPC workloads.
 * Strong experience with Linux.
 * Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment.

Ways to stand out from the crowd:

 * Expertise in related technology and passion for what you do. Experience with CUDA programming and NVIDIA GPUs. Knowledge of high-performance networks like InfiniBand, RoCE, NVLink, etc.
 * Experience with Deep Learning Frameworks such PyTorch, TensorFlow, etc. Knowledge of deep learning parallelisms and mapping to the communication subsystem. Experience with HPC applications.
 * Strong collaborative and interpersonal skills and a proven track record of effectively guiding and influencing within a dynamic and multi-functional environment.

## Description

* Investigate opportunities to improve communication performance by identifying bottlenecks in today's systems.
 * Design and implement new communication technologies to accelerate AI and HPC workloads.
 * Explore innovative solutions in HW and SW for our next generation platforms as part of co-design efforts involving GPU, Networking, and SW architects.
 * Build proofs-of-concept, conduct experiments, and perform quantitive modeling to evaluate and drive new innovations.
 * Use simulation to explore performance of large GPU clusters (think scales of 100s of 1000s of GPUs)


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