ML Infrastructure Engineer

BV Teck; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies seeks an experienced ML Infrastructure Engineer to design, build, and operate the platform layer powering large-scale AI training and inference workloads. You will work with GPU clusters, scheduler and storage systems to ensure reliability, efficiency, and cost control across hybrid configurations.

The role focuses on integrating PyTorch, DeepSpeed, JAX, and other tools, while improving developer experience for ML researchers.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related field.
• Six+ years of infrastructure, platform, or HPC engineering experience.
• Hands-on experience operating GPU clusters or large-scale ML training infrastructure.
• Strong proficiency in Python and at least one systems language (Go or C++).
• Deep understanding of distributed training, accelerator architectures, and collective communication.
• Experience with Kubernetes, Slurm, Ray, or similar ML scheduling systems.
• Strong Linux, networking, and high-performance storage knowledge.
• Experience with major cloud ML infrastructure offerings.
• Strong software engineering practices including testing, CI/CD, and code reviews.
• 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, queuing, and resource-sharing systems to maximize accelerator utilization.
• Integrate frameworks like PyTorch, JAX, DeepSpeed, and Ray Train into a unified platform.
• Operate high-performance storage systems and data pipelines for training data at near-line-rate.
• Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth communication.
• Implement observability for AI workloads including utilization and failure analytics.
• Develop tooling to enable researchers to launch experiments safely and efficiently.
• Drive cost optimization across compute, storage, and networking through scheduling and spot capacity.

Skills

Python proficiency

Distributed training concepts

Linux proficiency

Cross-functional collaboration

Education

Bachelor’s or Master’s degree in Computer Science

Tools

GPU clusters

Kubernetes

Slurm

Ray

PyTorch

DeepSpeed

FSDP

Megatron-LM

NCCL

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.