Senior AI/HPC Middleware Engineer — Remote

Cornelis Networks, Inc.

United States

Remote

USD 180,000 - 280,000

Full time

2 days ago
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Benefits offered by this job

Equity
Health benefits
401(k) match

Job summary

Cornelis Networks, Inc. is seeking a Senior Software Engineer to drive design, development, and upstream enablement of AI/HPC communication middleware.

This role emphasizes enabling and optimizing HPC middleware (MPI/SHMEM) and AI middleware (collective libraries) over Cornelis Networks interconnects. You will collaborate across kernel/driver, switches, systems, and application teams to push performance, correctness, and deployability on real workloads, while contributing upstream to open-source

Qualifications

  • 6+ years of systems programming on Linux.
  • Experience with HPC middleware internals or AI middleware optimization.
  • Ability to diagnose performance issues with profiling/tracing tools.
  • Experience contributing to open-source projects.

Responsibilities

  • Implement and optimize HPC middleware (HPI/SHMEM) and AI middleware (CCL/NCCL/RCCL).
  • Assist in integrating middleware with libfabric/OFI, UCX, or verbs-like layers.
  • Prepare and submit upstream contributions across MPI/SHMEM and related ecosystems.
  • Collaborate with kernel/driver and switch teams for end-to-end performance improvements.
  • Analyze performance traces and reproduce customer issues to guide fixes.

Skills

C/C++ programming
Linux system programming
Performance profiling
Open-source contributions

Education

Bachelor's degree in Computer Science or related field

Tools

MPI/Open MPI
NCCL/RCCL
libfabric/OFI
UCX

Job description

Cornelis Networks, Inc. is seeking a Senior Software Engineer to drive design, development, and upstream enablement of AI/HPC communication middleware.

This role emphasizes enabling and optimizing HPC middleware (MPI/SHMEM) and AI middleware (collective libraries) over Cornelis Networks interconnects. You will collaborate across kernel/driver, switches, systems, and application teams to push performance, correctness, and deployability on real workloads, while contributing upstream to open-source

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