AI Performance Engineer

Cornelis Networks is seeking an AI Performance Engineer to optimize training and multi-node inference across next-gen networking silicon and systems. The ideal candidate will have experience with AI/ML at cluster scale, a deep understanding of message passing, collectives, and bottleneck hunting, and hands-on experience with distributed training and inference architectures.

Requirements

• B.S. in CS/EE/CE/Math or related
• 5–7+ years running AI/ML at cluster scale
• Proven ability to set up, run, and analyze AI benchmarks
• Deep intuition for message passing, collectives, scaling efficiency, and bottleneck hunting for both training and low-latency serving
• Hands-on with distributed training beyond single-GPU (DP/TP/PP, ZeRO, FSDP, sharded optimizers) and distributed inference architectures (replicated vs sharded, tensor/KV parallel, MoE)
• Practical experience across AI stacks & comms: PyTorch, DeepSpeed, Megatron-LM, PyTorch Lightning; RCCL/NCCL, MPI/Horovod; Triton Inference Server, vLLM, TensorRT-LLM, Ray Serve, KServe
• Comfortable with compilers (GCC/LLVM/Intel/OneAPI) and MPI stacks; Python + shell power user
• Familiarity with network architectures (Omni-Path/OPA, InfiniBand, Ethernet/RDMA/ROCE) and Linux systems at the performance-tuning level, including NIC offloads, CQ moderation, pacing, ECN/RED
• Excellent written and verbal communication—turn measurements into persuasion with SLO-driven narratives for inference

Benefits

• Medical, dental, and vision coverage
• Disability and life insurance
• Dependent care flexible spending account
• Accidental injury insurance
• Pet insurance
• Generous paid holidays
• 401(k) with company match
• Open Time Off (OTO) for regular full-time exempt employees
• Sick time
• Bonding leave
• Pregnancy disability leave