AI Performance Optimization Engineer

Job Summary

Focused on maximizing throughput and minimizing latency, the full-time AI Performance Optimization Engineer will work remotely to optimize AI training and inference pipelines while collaborating with cross-functional teams to implement performance improvements across large neural network systems.

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
• Drive compiler-level optimizations and collaborate with ML and platform engineering teams to embed best practices in standard pipelines

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