Remote AI Optimization & Performance Engineer

Bright Vision Technologies; United States; Remote; USD 90,000 - 110,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Optimization Engineer for 100% remote work in the United States. You will focus on maximizing throughput, minimizing latency, and reducing cost across training and inference workloads for large neural network systems.

The role requires deep GPU architecture knowledge, model parallelism, memory management, and compiler-level optimization, with a proven track record shipping production AI workloads and strong measurement discipline.

Qualifications

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field.
• Six+ 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.

Responsibilities

• Optimize throughput, reduce latency, and lower cost across training and inference workloads for large neural networks.
• Work across the full stack from kernel optimization to distributed system tuning.
• Collaborate with product, design, engineering, operations and stakeholders to translate requirements into engineered solutions.
• Contribute to code reviews, design reviews, and mentorship of junior engineers.

Skills

Python

C++

Performance engineering

ML systems

Measurement/telemetry

Communication

Education

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

Tools

Profiling tools

GPU optimization

Triton/CUTLASS

TensorRT/DeepSpeed

Job description

Bright Vision Technologies is seeking an AI Optimization Engineer for 100% remote work in the United States. You will focus on maximizing throughput, minimizing latency, and reducing cost across training and inference workloads for large neural network systems.

The role requires deep GPU architecture knowledge, model parallelism, memory management, and compiler-level optimization, with a proven track record shipping production AI workloads and strong measurement discipline.