Remote AI Performance Engineer - Tune Training & Inference

Bright Vision Technologies; Troy (MI); Remote; USD 75,000 - 100,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Performance Engineer to optimize throughput, minimize latency, and reduce cost across training and inference workloads for large neural network systems. The role spans the full stack from kernel optimization to distributed system tuning.

The ideal candidate will have deep GPU architecture knowledge, model parallelism experience, memory management expertise, and compiler-level optimization skills, with a proven track record of shipping production AI

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.
• 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.

Responsibilities

• Extract maximum throughput across training and inference workloads.
• Minimize latency and reduce cost for large neural networks.
• Tune end-to-end systems from kernel optimization to distributed scheduling.
• Mentor junior engineers and participate in design reviews.

Skills

Python

C++

Distributed training

Performance engineering

Measurement & analytics

Education

Bachelor’s or Master’s degree in CS/CE

Tools

Profiling tools

Triton

CUTLASS

TensorRT

Job description

Bright Vision Technologies is seeking an AI Performance Engineer to optimize throughput, minimize latency, and reduce cost across training and inference workloads for large neural network systems. The role spans the full stack from kernel optimization to distributed system tuning.

The ideal candidate will have deep GPU architecture knowledge, model parallelism experience, memory management expertise, and compiler-level optimization skills, with a proven track record of shipping production AI