Principal Deep Learning Communication Architect

Nvidia Corporation

Multiple Locations
Base: 272,000 usd - 431,250 usd; bonus/equity: eli...
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Ai communication library design
Heterogeneous interconnects
Trillion-parameter ai
** NVIDIA Corporation is seeking a Principal Deep Learning Communication Architect to lead the development and optimization of communication libraries for their next-generation platforms, focusing on high-performance computing and distributed deep learning. The ideal candidate will possess a strong background in parallel computing, communication primitives, and NVIDIA GPU architecture, complemented by extensive industry experience. **

Job Summary

  • Define the long-term technical roadmap for communication libraries across NVIDIA’s next-generation platforms, ensuring seamless scaling of models to clusters comprising hundreds of thousands of nodes.
  • Lead the development of next-generation communication primitives and collective algorithms, optimizing for heterogeneous interconnects such as NVLink, Spectrum-X (Ethernet), and Quantum-X (InfiniBand).
  • Collaborate with silicon architects and software engineers to influence hardware specifications for next-generation networking, ensuring they meet the evolving demands of trillion-parameter LLMs and Agentic AI.

Matching Summary

Match Score: 75

** NVIDIA Corporation is seeking a Principal Deep Learning Communication Architect to lead the development and optimization of communication libraries for their next-generation platforms, focusing on high-performance computing and distributed deep learning. The ideal candidate will possess a strong background in parallel computing, communication primitives, and NVIDIA GPU architecture, complemented by extensive industry experience. **

Salary

Base: 272,000 USD - 431,250 USD; Bonus/Equity: Eligible for equity; Benefits: Eligible for benefits

Skills & Requirements

Must-have

  • AI communication library design
  • heterogeneous interconnects
  • trillion-parameter AI
  • hardware/software co-design
  • quantitative modeling

Nice-to-have

  • framework development experience
  • upstream open-source contributions
  • deploying models on NVIDIA platforms
  • strong portfolio of patents or papers

Key Requirements

  • Ph.D. or M.S. in Computer Science, Electrical Engineering, or related field (or equivalent experience)
  • 12+ years of industry experience in HPC or distributed deep learning
  • Deep understanding of 3D parallelism
  • Technical proficiency with NCCL, UCX, UCC, NVSHMEM, or MPI
  • Experience with RDMA, RoCE, and low-level InfiniBand verbs
  • Advanced knowledge of high-throughput inference engines and schedulers
  • Expert knowledge of NVIDIA GPU memory hierarchy and CUDA programming models

Work Rights

Not specified

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