Remote ML Infra Engineer: GPU Clusters & Scale

Bright Vision Technologies; South Jordan, Draper (UT, UT); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Infrastructure Engineer to design, deploy, and operate large-scale GPU clusters for AI training and inference. You will build scalable scheduling, data pipelines, and observability, ensuring reliability and cost control across research and production workloads.

The role emphasizes collaboration with ML researchers, software engineers, and platform teams, with a focus on performance, security, and multi-tenant isolation in a remote-first environment.

Qualifications

• 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++.
• Experience with Kubernetes, Slurm, Ray, or similar scheduling systems for ML workloads.
• Strong Linux internals, networking, and high-performance storage knowledge.
• Excellent communication and cross-functional collaboration skills.

Responsibilities

• Design and operate GPU and accelerator infrastructure for training and inference.
• Build scheduling, queueing, and resource-sharing systems across teams.
• Integrate PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into a platform.
• Operate high-performance storage systems and data pipelines for training data throughput.
• Design networking architectures supporting RDMA, InfiniBand, NCCL, and high bandwidth communications.
• Develop tooling and runbooks for researchers to launch experiments safely.

Skills

Python

Go/C++

Distributed training

Kubernetes

Ray

Linux internals

CI/CD

Networking

Education

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

Tools

Slurm

Ray

DeepSpeed

Job description

Bright Vision Technologies is seeking an AI Infrastructure Engineer to design, deploy, and operate large-scale GPU clusters for AI training and inference. You will build scalable scheduling, data pipelines, and observability, ensuring reliability and cost control across research and production workloads.

The role emphasizes collaboration with ML researchers, software engineers, and platform teams, with a focus on performance, security, and multi-tenant isolation in a remote-first environment.