About this role

Optimize training and multi-node inference across next-gen networking silicon and systems
Partner with architecture, firmware, software, and lighthouse customers
Own end-to-end performance for distributed AI workloads
Benchmark, characterize, and tune open-source & industry workloads
Design and optimize distributed serving topologies
Build crisp proof points that compare Cornelis Omni-Path to competing interconnects
Evangelize best practices on distributed inference patterns and anti-patterns

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
Hands-on with distributed training and inference architectures
Practical experience across AI stacks & comms: PyTorch, DeepSpeed, etc.
Comfortable with compilers and MPI stacks
Familiarity with network architectures and Linux systems at performance-tuning level
Excellent written and verbal communication skills

AI Performance Engineer

Key skills