ML Infrastructure Engineer

Bright Vision Technologies; Eden Prairie (MN); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an ML Infrastructure Engineer for 100% remote work in the U.S., focusing on GPU clusters, distributed training, and a unified AI platform. You will design and operate the platform layer powering large-scale AI workloads, with emphasis on reliability, efficiency, and cost control.

The role requires 6+ years of experience, strong software engineering practices, and collaboration with research and ML teams to plan capacity and ensure robust infrastructure for

Qualifications

• Degree in computer science or related field.
• 6+ years in infrastructure, platform, or HPC engineering.
• Hands-on GPU clusters or large-scale ML infrastructure experience.
• Proficiency in Python and at least one systems language (Go or C++).
• Distributed training, accelerator architectures, and collective communication.
• Experience with Kubernetes, Slurm, Ray for ML workloads.
• Strong Linux, networking, and high-performance storage knowledge.
• Experience with cloud ML infrastructure offerings from major providers.
• CI/CD, testing, and code review practices.
• Excellent communication and cross-functional collaboration.

Responsibilities

• Design and operate GPU and accelerator infrastructure for training and inference.
• Build scheduling, queueing, and resource-sharing systems for accelerator utilization.
• Integrate frameworks like PyTorch, JAX, DeepSpeed, Megatron-LM, and Ray Train.
• Operate high-performance storage and data pipelines for near-line-rate data.</li>
• Design networking architectures with RDMA, InfiniBand, NCCL.
• Build observability for AI workloads including utilization and training stability.
• Implement checkpointing, restart, and fault-tolerance patterns 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.
• Plan capacity with research and applied ML teams.
• Implement security, isolation, and multi-tenant access controls.
• Automate cluster provisioning, lifecycle management, and configuration enforcement.
• Maintain runbooks and capacity dashboards for the AI platform.
• Stay current with AI infra research and open-source tooling.

Skills

Python

Go

C++

Kubernetes

Slurm

Ray

Linux

Networking

Storage

Cloud ML

CI/CD

Collaboration

Communication

Education

Bachelor’s or Master’s in CS

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 paved­road 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.

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.