Remote GPU ML Infrastructure Engineer

Bright Vision Technologies; Highlands Ranch (CO); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an AI infrastructure engineer to design, build, and operate the platform layer powering large-scale ML training and inference workloads. You will focus on GPU clusters, scheduling, storage, and developer experience for ML engineers and researchers.

The ideal candidate has built or operated production AI infrastructure at scale, understands hardware–kernel–ML framework interactions, and applies strong software engineering practices to platform work.

Qualifications

• 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.
• 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.

Responsibilities

• Design and operate GPU and accelerator infrastructure for training and inference.
• Build scheduling, queuing, and resource-sharing systems for accelerator utilization.
• Integrate frameworks like PyTorch, JAX, DeepSpeed, and Megatron-LM into a unified platform.
• Operate high-performance storage systems and data pipelines for training data throughput.
• Design networking architectures supporting RDMA, InfiniBand, NCCL.
• Develop observability for AI workloads including utilization and throughput.
• Implement checkpointing and fault-tolerance for long-running training jobs.
• Drive cost optimization across compute, storage, and networking.
• Develop developer tooling to simplify researcher workflows.
• Partner with research and ML teams to plan capacity for training runs.
• Implement security controls and multi-tenant isolation.
• Automate cluster provisioning and lifecycle management.
• Maintain runbooks and capacity dashboards for the AI platform.
• Stay current with AI infra research and tooling.

Skills

Python

Go

C++

Distributed systems

Kubernetes

Slurm

Ray

Linux

CI/CD

Communication

Education

Bachelor’s or Master’s in Computer Science or related

Tools

Kubernetes

Slurm

Ray

CI/CD tooling

Job description

Bright Vision Technologies is seeking an AI infrastructure engineer to design, build, and operate the platform layer powering large-scale ML training and inference workloads. You will focus on GPU clusters, scheduling, storage, and developer experience for ML engineers and researchers.

The ideal candidate has built or operated production AI infrastructure at scale, understands hardware–kernel–ML framework interactions, and applies strong software engineering practices to platform work.