AI Performance Optimization Engineer

Job Summary

To support a growing team, the full-time AI Performance Optimization Engineer will focus on optimizing AI training and inference workloads for large neural network systems while working remotely in a direct W2 engagement.

Key Responsibilities

• Profile and optimize end-to-end AI training and inference pipelines for throughput, latency, and cost
• Identify and eliminate bottlenecks across data loading, model compute, communication, and memory
• Implement and tune quantization, sparsity, and pruning strategies to reduce model footprint and accelerate inference

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field
• Six or more years of experience in performance engineering, ML systems, or HPC
• Strong proficiency in Python and C++
• Hands-on experience optimizing deep learning workloads on modern GPUs
• Deep understanding of distributed training and inference techniques