Remote ML Infrastructure Engineer — Scale AI Training

Bright Vision Technologies; Sugar Land (TX); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies in the United States is seeking an AI-focused ML Infrastructure Engineer to design, build, and operate the platform layer powering large-scale AI training and inference workloads. You will oversee GPU clusters, scheduling systems, and high-performance storage to ensure reliable, cost-efficient operations for researchers and engineers.

The role emphasizes tight integration of ML frameworks (PyTorch, JAX, DeepSpeed, Ray Train), strong Linux and network expertise, and

Qualifications

• Bachelor’s or Master’s degree in Computer Science or 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.

Responsibilities

• Design and operate GPU and accelerator infrastructure for training and inference across on-prem, cloud, and hybrid setups.
• Build scheduling and resource-sharing systems to maximize accelerator utilization across teams.
• Integrate frameworks like PyTorch, JAX, DeepSpeed, and Ray Train into a unified platform.
• Operate high-performance storage systems and data pipelines for near-line-rate data feeding.
• Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth communication.
• Develop observability for AI workloads including utilization, throughput, and failure analysis.
• Implement checkpointing, restart, and fault-tolerance for long-running training jobs.
• Drive cost optimization across compute, storage, and networking via scheduling and spot capacity.
• Develop developer tooling and paved-road workflows for researchers to launch experiments safely.
• Collaborate with research teams to plan capacity for upcoming training runs.
• Implement security controls and multi-tenant isolation for AI infra.
• Automate cluster provisioning, lifecycle management, and configuration enforcement.
• Maintain runbooks, capacity dashboards, and operational docs for the AI platform.
• Stay current with AI infra research, accelerator hardware, and open-source tooling.

Skills

Python

Go/C++

Linux

Kubernetes

Slurm

Ray

Distributed training

CI/CD

Education

Bachelor’s/Master’s in CS or related field

Tools

Kubernetes

Slurm

Ray

Job description

Bright Vision Technologies in the United States is seeking an AI-focused ML Infrastructure Engineer to design, build, and operate the platform layer powering large-scale AI training and inference workloads. You will oversee GPU clusters, scheduling systems, and high-performance storage to ensure reliable, cost-efficient operations for researchers and engineers.

The role emphasizes tight integration of ML frameworks (PyTorch, JAX, DeepSpeed, Ray Train), strong Linux and network expertise, and