AI Performance Engineer

To optimize training and multi-node inference across next-gen networking silicon, the full-time AI Performance Engineer will measure and enhance end-to-end performance for distributed AI workloads while working remotely within the United States. Key Responsibilities Measure and optimize end-to-end performance for distributed AI workloads across CPU and GPU clusters Benchmark, characterize, and tune open-source and industry workloads on various compute and network hardware Design and optimize distributed serving topologies and validate performance during inference Required Qualifications BS in CS/EE/CE/Math or related field (MS preferred) with 2-3+ years of experience in AI/ML at cluster scale Proven ability to set up, run, and analyze AI benchmarks with a deep understanding of message passing and scaling efficiency Hands-on experience with distributed training and inference architectures Practical knowledge of AI stacks and communication frameworks such as PyTorch and DeepSpeed Familiarity with network architectures and Linux systems at the performance-tuning level