Remote AI Performance & Infrastructure Engineer

Bright Vision Technologies; United States; A distancia; USD 100.000 - 150.000; Jornada completa

Descripción de la vacante

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. We are seeking an AI Infrastructure Engineer to optimize AI workloads, spanning kernel to distributed systems, in a 100% remote role.

The ideal candidate has a strong background in Python/C++, GPU optimization, and distributed training, with production experience and collaboration across product, design, engineering, and operations.

Formación

• Bachelor's or master's degree in computer science, Computer Engineering, or 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.

Conocimientos

Python

C++

GPU optimization

Distributed training

Profiling

Memory hierarchies

Debugging

Communication

Educación

Bachelor's or Master's in CS/CE

Herramientas

Triton

CUTLASS

TensorRT-LLM

DeepSpeed

vLLM integration

Descripción del empleo

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. We are seeking an AI Infrastructure Engineer to optimize AI workloads, spanning kernel to distributed systems, in a 100% remote role.

The ideal candidate has a strong background in Python/C++, GPU optimization, and distributed training, with production experience and collaboration across product, design, engineering, and operations.