• Design and implement performance-critical features for CCL enablement on Cornelis Networks’ fabrics • 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 • Tune AI frameworks such as PyTorch Distributed, TensorFlow/XLA, JAX, DeepSpeed, and Megatron-LM • Develop benchmarks and microbenchmarks aligned with real model performance • Contribute upstream to AI communication and distributed training projects • Participate in design reviews, code reviews, CI, and long-term maintenance • Prototype and validate Ultra Ethernet capabilities for AI collective communication • Provide technical input for deployment considerations and performance validation • Collaborate with kernel/driver, switch, performance, and systems teams • Support advanced escalations by analyzing traces and providing robust fixes