Remote AI Optimization Engineer

Bright Vision Technologies; Oregon; Hybrid; USD 85,000 - 115,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Optimization Engineer to maximize throughput, minimize latency, and cut costs across training and inference for large neural networks. Work spans kernel optimization to distributed system tuning with GPU architecture and memory management focus.

The role requires rigorous, data-driven optimization, collaboration with product, design, engineering, operations, and business stakeholders, and mentorship of junior engineers to raise engineering standards.

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

• Focus on extracting maximum throughput, minimizing latency, and reducing cost across training and inference workloads.
• Collaborate with cross-functional partners to translate ambiguous requirements into well-engineered solutions.

Skills

Python

C++

GPU optimization

Distributed training

Performance engineering

ML systems

HPC

Profiling skills

Debugging & analytics

Communication

Education

Bachelor's or Master's in CS/CE or related field

Tools

Triton

CUTLASS

TensorRT-LLM

DeepSpeed

vLLM

Job description

Bright Vision Technologies is seeking an AI Optimization Engineer to maximize throughput, minimize latency, and cut costs across training and inference for large neural networks. Work spans kernel optimization to distributed system tuning with GPU architecture and memory management focus.

The role requires rigorous, data-driven optimization, collaboration with product, design, engineering, operations, and business stakeholders, and mentorship of junior engineers to raise engineering standards.