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Senior Software Architect, AI and GPU Networking

NVIDIA·Израиль·en
В офисеПолная занятостьSoftware ArchitectureComputer HardwareEnterprise Software

Lead architecture and development for AI networking technologies that accelerate workloads in modern data centers. The role combines system research, hands-on software development, hardware co-design, and proof-of-concept delivery.

Обязанности

  • Enhance GPU networking technologies for AI workloads, including runtime and communication solutions.
  • Co-design GPU, DPU, and interconnect hardware features that accelerate data movement and enable inference and model-serving capabilities.
  • Research and evaluate technologies, innovations, and partnerships against technical roadmaps and business value.
  • Lead architecture and design for runtime systems, communication libraries, and AI-specific technologies.
  • Lead proof-of-concept development to assess and advance new technologies.
  • Explore transport functions, AI system communication, distributed AI, deep learning, HPC, software-defined networking, virtualization, and storage.

Требования

  • M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or Computer Engineering, or equivalent experience.
  • At least 5 years of industry experience in system architecture, AI systems architecture, AI scaling, AI framework parallelism, or deep learning training workloads.
  • Experience with algorithm design, system programming, computer architecture, and operating systems.
  • Experience with virtualization, networking, and storage.
  • Strong understanding of performance profiling, optimization, and hardware feature definition and use.
  • Strong programming and software development skills.
  • Ability to work and communicate effectively across multinational, multi-time-zone teams.

Будет плюсом

  • Research track record.
  • Experience with CPU, GPU, memory, storage, or networking architecture.
  • Knowledge of deep learning frameworks and AI communication libraries such as NCCL, UCX, or MPI.
  • Understanding of inference and training workload optimization, including prefill/decode and parallelism techniques.
  • Strong communication skills.

Соответствие

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