Senior Software Architect - Deep Learning and Hpc Communications

NVIDIA NVIDIA · Semiconductors · Germany +4 · Remote

This role focuses on architecting and implementing next-generation communication software and platforms for deep learning and high-performance computing (HPC) applications, specifically targeting the efficient scaling of GPU clusters. The work involves identifying performance bottlenecks, designing new communication technologies, exploring hardware/software co-design, and using simulation to evaluate performance at massive scales.

What you'd actually do

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

Skills

Required

  • C/C++ programming
  • parallel programming models (MPI, SHMEM)
  • communication runtimes (MPI, NCCL, NVSHMEM, OpenSHMEM, UCX, UCC)
  • operating systems
  • computer and system architecture
  • network architecture
  • Linux

Nice to have

  • CUDA programming
  • NVIDIA GPUs
  • high-performance networks (InfiniBand, RoCE, NVLink)
  • Deep Learning Frameworks (PyTorch, TensorFlow)
  • deep learning parallelisms
  • HPC applications

What the JD emphasized

  • M.S./Ph.D. degree in CS/CE or equivalent experience
  • 3+ 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

Other signals

  • GPU communication libraries
  • scaling Deep Learning and HPC applications
  • next-gen data center platforms
  • scalable communications software
  • tens of thousands of GPUs
  • high-speed interconnects
  • high-speed networking
  • Efficient and fast communication between GPUs
  • end-to-end application performance
  • advance the state-of-the-art
  • break performance barriers
  • deliver platforms the world has never seen before