AI Infrastructure Engineer

Bright Vision Technologies•East Brunswick, NJ

1d•$100,000 - $160,000•Remote

About The Position

Bright Vision Technologies is seeking an AI Infrastructure Engineer to design, build, and operate the platform layer powering 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 an emphasis on reliability, efficiency, and cost control. The position is 100% remote within the United States and is full-time, direct W2, with an annual salary range of $100,000–$160,000. Candidates with 10 or more years of relevant experience are sought. The company cannot sponsor new H-1B visa petitions, but U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply. The engineer will work across on-premises clusters, cloud-managed services, and hybrid configurations. They will partner with research and applied ML teams to plan infrastructure capacity, develop reliable and efficient platform workflows, and maintain operational documentation. Bright Vision Technologies is an Equal Opportunity Employer and states that it prohibits discrimination and workplace harassment.

Requirements

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.

Nice To Haves

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.

Responsibilities

Design and operate GPU and accelerator infrastructure for training and inference across on-premises clusters, cloud-managed services, and hybrid configurations.
Build scheduling, queueing, and resource-sharing systems to maximize accelerator utilization across teams.
Integrate frameworks such as PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into a unified platform.
Operate high-performance storage systems and data pipelines that supply training data to accelerators 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.
Optimize costs across compute, storage, and networking using scheduling, spot capacity, and right-sizing.
Develop developer tooling and paved-road workflows that help 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.
Automate 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.