Intelligent Infrastructure Engineer

Bright Vision Technologies • Orlando, FL

15h • $100,000 - $150,000 • Remote

About The Position

Bright Vision Technologies is seeking an Intelligent Infrastructure Engineer to design, build, and operate the platform layer supporting large-scale AI training and inference workloads. The role focuses on GPU clusters, distributed training frameworks, scheduling, storage performance, and developer experience for ML engineers and researchers, with an emphasis on reliability, efficiency, and cost control. The ideal candidate has built or operated production AI infrastructure at scale, understands how hardware, kernels, schedulers, and ML frameworks interact, and applies strong software engineering practices to platform work.

This is a full-time, direct W2 position that is 100% remote within the United States. The annual salary range is $100,000–$150,000. U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply; the company cannot sponsor new H-1B visa petitions for this position.

Requirements

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

Nice To Haves

• Experience operating InfiniBand or RDMA networking at scale.
• Contributions to open-source ML infrastructure projects.
• Familiarity with custom orchestrators or research-grade training stacks.
• Exposure to frontier model training operations.
• Experience with FinOps for AI workloads.

Responsibilities

• Design, build, and operate platform infrastructure for large-scale AI training and inference workloads.
• Support GPU clusters, distributed training frameworks, workload scheduling, and storage performance.
• Develop and maintain infrastructure that provides an effective experience for ML engineers and researchers.
• Emphasize reliability, efficiency, and cost control in AI infrastructure operations.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior