Cornelis Networks, Inc.

Cornelis Networks, Inc.

Senior Software Engineer – AI Middleware

Austin, Texas, United States · Senior · Full-time

Sponsorship not specifiedDetected 16 hours ago
C++CNode.jsCode ReviewTensorFlowPyTorchCommunication

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About the role

  • Our differentiated architecture seamlessly integrates hardware, software and system level technologies to maximize the efficiency of GPU, CPU and accelerator-based compute clusters at any scale.
  • As a global organization, our team spans multiple U.S. states and six countries, and we continue to expand with exceptional talent in onsite, hybrid, and fully remote roles.
  • This role focuses on distributed AI workloads and enabling/optimizing collective communication libraries (e.g., NCCL/RCCL) over Cornelis Networks' interconnects.

Responsibilities

  • Design and implement performance-critical features for CCL enablement on Cornelis Networks' fabrics.
  • Develop benchmarks and microbenchmarks aligned with real model performance.
  • Participate in design reviews, code reviews, CI, and long-term maintenance.
  • Prototype and validate Ultra Ethernet capabilities for AI collective communication.
  • Collaborate with kernel/driver, switch, performance, and systems teams.
  • Support advanced escalations by analyzing traces and providing robust fixes.

Requirements

  • 8+ years of experience in high-performance systems programming in C/C++ on Linux.
  • Strong experience with GPU communication stacks including CUDA/ROCm and NCCL/RCCL.
  • Experience delivering production-quality code.

Nice to have

  • Experience with AI frameworks such as PyTorch Distributed, DeepSpeed, and Megatron-LM.
  • Familiarity with libfabric/OFI, UCX, and RDMA concepts.
  • Experience with RoCEv2 and Ultra Ethernet.
  • Cornelis Networks does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services.
  • We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process.

Skills

  • Optimize distributed training performance across multi-node, multi-GPU configurations.
  • Improve GPU communication paths including GPU-direct transfers, IPC, and CPU/GPU synchronization.
  • Profile distributed AI workloads and identify bottlenecks across the software and hardware stack.

Compensation

  • We offer a competitive compensation package that includes equity, cash, and incentives, along with health and retirement benefits.

Company info

  • Backed by top-tier venture capital and strategic investors, we are committed to innovation, performance and scalability - solving the world's most demanding computational challenges with our next-generation networking solutions.
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