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.
Experience with profiling tools across CPU, GPU, and distributed systems.
Familiarity with model compression techniques and their accuracy implications.
Strong grasp of memory hierarchies, communication primitives, and parallelism strategies.
Excellent measurement, debugging, and analytical reasoning skills.
Strong communication and collaboration skills.
Experience optimizing LLM inference at production scale.
Contributions to vLLM, TensorRT-LLM, DeepSpeed, or similar projects.
Familiarity with custom kernel authoring in Triton or CUTLASS.
Experience with FinOps for AI workloads.
Publications or talks on AI systems performance.

Model Optimization Engineer

Summary