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Remote Ai Infrastructure Engineer Jobs in Boston, MA

MLabs

Senior Infrastructure Engineer

Boston, MA · Remote

$170K - $220K/yr

Remote - Remote - Based In ET+2 / -3, NY Preferred Remote | Full-time Compensation: $170K - $220K ... They are currently seeking a Senior Infrastructure Engineer to design and build an internal ...

MLabs

Senior Infrastructure Engineer

Boston, MA · Remote

$170K - $220K/yr

Remote - Remote - Based In ET+2 / -3, NY Preferred Remote | Full-time Compensation: $170K - $220K ... They are currently seeking a Senior Infrastructure Engineer to design and build an internal ...

Wsgr

Principal Cloud Infrastructure Engineer

Boston, MA · On-site +1

$147K - $198K/yr

The Principal Cloud Infrastructure Engineer, under the direction of the Senior manager of ... This role can be hybrid or virtual/remote. Essential Duties and Responsibilities: * Act as the ...

NAVA Software Solutions

AI Engineer - Remote

Boston, MA · On-site +1

AI Engineer Location: 100% Remote Duration: 6+ month contract-to-hire Interviews: 2 rounds Top ... Guide architecture, infrastructure, and tools for AI/ML products. * Develop rapid prototypes and ...

ICONSTAFF

Senior Platform Engineer

Cambridge, MA · Remote

$120K - $200K/yr

Senior Platform/Infrastructure Engineer Location: Fully remote (HQ Cambridge, MA) Hours: 9-5 EST ... The role blends infrastructure ownership with platform engineering to enable AI/product teams to ...

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Remote Ai Infrastructure Engineer information

See Boston, MA salary details

$50.5K

$138K

$197.7K

How much do remote ai infrastructure engineer jobs pay per year?

As of Aug 11, 2026, the average yearly pay for remote ai infrastructure engineer in Boston, MA is $138,045.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,800.00 and $153,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a remote AI infrastructure engineer?

To thrive as a Remote AI Infrastructure Engineer, you need expertise in cloud computing, distributed systems, and software engineering, often supported by a degree in computer science or a related field. Familiarity with tools like Kubernetes, Docker, Terraform, and cloud platforms such as AWS, Azure, or GCP is typically required, along with knowledge of CI/CD pipelines and AI/ML frameworks. Strong problem-solving skills, self-motivation, and effective remote communication are essential soft skills for success in this role. These skills ensure robust, scalable AI infrastructure that supports rapid innovation and seamless collaboration across distributed teams.

What is a remote AI infrastructure engineer?

A Remote AI Infrastructure Engineer is a professional who designs, builds, and maintains the systems and tools necessary to support artificial intelligence (AI) projects, all while working remotely. Their responsibilities often include developing and optimizing cloud or on-premise infrastructure, ensuring scalability, managing data pipelines, and supporting machine learning workflows. They work closely with data scientists and software engineers to ensure AI models can be efficiently trained, deployed, and monitored in production environments. The remote aspect allows them to perform these tasks from anywhere, using collaboration tools and cloud platforms.

What are some common challenges faced by remote AI infrastructure engineers, and how can they be addressed?

Remote AI Infrastructure Engineers often encounter challenges such as managing distributed systems, ensuring robust data pipelines, and maintaining high system reliability across different time zones. Collaboration with cross-functional teams can require clear communication and effective use of remote tools. To address these challenges, it's important to establish strong documentation practices, schedule regular check-ins, and utilize automated monitoring and deployment solutions. Staying proactive and adaptable helps ensure seamless infrastructure performance and team alignment.
What are the most commonly searched types of Ai Infrastructure Engineer jobs in Boston, MA? The most popular types of Ai Infrastructure Engineer jobs in Boston, MA are:
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Remote Ai Infrastructure Engineer jobs near you

AI Infrastructure Engineer

Bright Vision Technologies

Lexington, MA • On-site, Remote

$100K - $160K/yr

Full-time

Posted 7 days ago

Job description

AI 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: AI Infrastructure Engineer
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $100,000–$160,000 Annually
Experience Required: 10+ years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, 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 an AI Infrastructure Engineer to design, build, and operate the 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, with strong emphasis 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 and operate GPU 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 accelerator utilization across many teams.
  • Integrate frameworks such as PyTorch, 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 including utilization, throughput, training stability, and failure-mode analytics.
  • Implement checkpointing, restart, and fault-tolerance patterns for long-running training jobs at scale.
  • Drive cost optimization across compute, storage, and networking through scheduling, spot capacity, and right-sizing.
  • Develop developer tooling 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 or Master’s degree in Computer Science or a related field.
  • Ten 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.
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 to frontier model training operations.
  • Experience with FinOps for AI workloads.
How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to venkat.r@bvteck.com or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com.
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.

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