Senior Model Optimization Engineer - Remote AI Performance

Bright Vision Technologies; United States; Remote; USD 150,000 - 175,000; Full time

Job summary

Bright Vision Technologies is seeking a Model Optimization Engineer to maximize throughput, minimize latency, and reduce cost across training and inference for large neural network systems. You will optimize from kernel-level code to distributed systems, working with GPU architectures, model parallelism, memory management, and compiler-level techniques.

The role requires 6+ years in performance engineering, deep learning workloads on GPUs, and strong Python/C++.

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.
• 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

• Optimize throughput, latency, and cost across training and inference workloads for large neural networks.
• Span the full stack from low-level kernel optimization to distributed system tuning.
• Work with cross-functional teams to translate requirements into engineered solutions.
• Mentor junior engineers and contribute to design and code reviews.

Skills

Python

C++

Performance engineering

GPU optimization

Distributed systems

Profiling tools

Education

Bachelor’s or Master’s degree in Computer Science or Computer Engineering

Tools

Triton

CUTLASS

TensorRT

Job description

Bright Vision Technologies is seeking a Model Optimization Engineer to maximize throughput, minimize latency, and reduce cost across training and inference for large neural network systems. You will optimize from kernel-level code to distributed systems, working with GPU architectures, model parallelism, memory management, and compiler-level techniques.

The role requires 6+ years in performance engineering, deep learning workloads on GPUs, and strong Python/C++.