Remote Senior AI Systems Engineer

Bright Vision Technologies; San Diego, 4S Ranch (CA, CA); Remote; USD 90,000 - 100,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Systems Engineer to design, build, and operate the platform layer powering large-scale AI training and inference workloads. The role emphasizes GPU clusters, distributed training frameworks, scheduling, and storage performance, with a focus on reliability and cost control.

The ideal candidate has operated production AI infrastructure at scale and brings strong software discipline to platform work.

Qualifications

• Bachelor’s or Master’s degree in CS or related field.
• 6+ years of infrastructure, platform, or HPC engineering.
• Hands-on experience operating GPU clusters or large-scale ML training infrastructure.
• Strong proficiency in Python and Go or C++.
• Understanding of distributed training, accelerator architectures, and CI/CD.
• Experience with Kubernetes, Slurm, Ray, or similar schedulers.
• Strong Linux internals, networking, and high-performance storage.
• Experience with cloud ML infrastructure offerings.
• Strong software engineering practices including testing and code reviews.
• Excellent communication and cross-functional collaboration.

Responsibilities

• Design, build, and operate the platform layer powering large-scale AI training and inference workloads.
• Focus on GPU clusters, distributed training frameworks, scheduling, and storage performance.
• Improve developer experience for ML engineers and researchers.
• Ensure reliability, efficiency, and cost control of AI infrastructure.
• Collaborate with hardware teams, kernel developers, schedulers, and ML frameworks.

Skills

Python

Go or C++

GPU clusters

Distributed training

CI/CD

Linux internals

Networking

Cloud ML infra

Communication

Education

Bachelor's or Master's in CS

Tools

Kubernetes

Slurm

Ray

CI/CD tools

InfiniBand

Job description

Bright Vision Technologies is seeking an AI Systems Engineer to design, build, and operate the platform layer powering large-scale AI training and inference workloads. The role emphasizes GPU clusters, distributed training frameworks, scheduling, and storage performance, with a focus on reliability and cost control.

The ideal candidate has operated production AI infrastructure at scale and brings strong software discipline to platform work.