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

ML Infrastructure Engineer -Remote

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

• Job Title: ML Infrastructure Engineer
• Location: 100% Remote (U.S.)
• Position Type: Full-time, Direct W2
• Salary Range: $100,000–$150,000 Annually
• Experience Required: 6+ years
• Sponsorship: U.S. Citizens, Green CardHolders, EADHolders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary

We are seeking anAI Infrastructure Engineerto design, build, andoperatethe platform layer that powers 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, withstrongemphasis on reliability, efficiency, and cost control. The ideal candidate has built or operated production AI infrastructure at scale, understands the interaction between hardware, kernel, scheduler, and ML framework, and brings strong software engineering discipline to platform work.

Key Responsibilities

• Design andoperateGPU and accelerator infrastructure for training and inference, spanning on-prem clusters, cloud-managed services, and hybrid configurations.
• Build scheduling, queueing, and resource-sharing systems that maximize acceleratorutilizationacross many teams.
• Integrate frameworks such asPyTorch, JAX,DeepSpeed, FSDP, Megatron-LM, and Ray Train into a unified platform offering.
• Operate high-performance storage systems and data pipelines that keep accelerators fed with training data at near-line-rate.
• Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth collective communication.
• Build observability for AI workloads includingutilization, throughput, training stability, and failure-mode analytics.
• Implement checkpointing, restart, and fault-tolerance patterns for long-running training jobs at scale.
• Drive cost optimization acrosscompute, storage, and networking through scheduling, spot capacity, and right-sizing.
• Developdevelopertooling and paved-road workflows that let researchers launch experiments safely and efficiently.
• Partner with research and applied ML teams to plan capacity for upcoming training runs.
• Implement security controls, isolation, and access management for multi-tenant AI infrastructure.
• Drive automation across cluster provisioning, lifecycle management, and configuration enforcement.
• Maintain runbooks, capacity dashboards, and operational documentation for the AI platform.
• Stay current with AI infrastructure research, accelerator hardware, and emerging open-source AI tooling.

Required Qualifications

• Bachelor’s orMaster’s degree in Computer Scienceor 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.
• Strongproficiencyin 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.

Preferred Qualifications

• 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 tofrontier model training operations.
• Experience with FinOps for AI workloads.

Bright Vision Technologies is an Equal Opportunity Employer.

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.