Senior Intelligent Infrastructure Engineer - Remote

Bright Vision Technologies; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an Intelligent Infrastructure Engineer to design, build, and operate the platform layer for large-scale AI workloads. You’ll focus on GPU clusters, distributed training frameworks, scheduling, storage performance, and developer experience for ML engineers and researchers, emphasizing reliability and cost efficiency.

The ideal candidate has built or operated production AI infrastructure at scale, understands hardware–kernel–scheduler–ML framework

Qualifications

• Bachelor’s or Master’s degree in CS or a related field.
• 6+ years of experience in infrastructure, platform, or HPC engineering.
• Hands-on experience operating GPU clusters or large-scale ML training infra.
• Strong Python and at least one systems language such as Go or C++.
• Deep understanding of distributed training and accelerator architectures.
• Experience with Kubernetes, Slurm, Ray for ML workloads.
• Strong Linux internals, networking, and high-performance storage.
• Experience with major cloud ML infrastructure offerings.
• Strong software engineering practices including testing, CI/CD, and code review.
• Excellent communication and cross-functional collaboration skills.

Skills

Python

Distributed training

Linux internals

Networking

CI/CD practices

Communication skills

Cross-functional collaboration

Education

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

Tools

Go

C++

Kubernetes

Slurm

Ray

Job description

Bright Vision Technologies is seeking an Intelligent Infrastructure Engineer to design, build, and operate the platform layer for large-scale AI workloads. You’ll focus on GPU clusters, distributed training frameworks, scheduling, storage performance, and developer experience for ML engineers and researchers, emphasizing reliability and cost efficiency.

The ideal candidate has built or operated production AI infrastructure at scale, understands hardware–kernel–scheduler–ML framework