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Lightning AI is seeking a Senior Network Engineer to design, deploy, and operate InfiniBand fabrics powering large AI GPU clusters. The role emphasizes building and maintaining high‑performance NVIDIA InfiniBand fabrics using UFM, NCCL, RoCEv2, and modern data center tech.
The ideal candidate has deep expertise in InfiniBand networking, NVIDIA UFM Enterprise, large‑scale GPU deployments, Linux networking, automation, and troubleshooting distributed AI workloads.
New York, New York, United States; Remote; San Francisco, California, United States; Seattle, Washington, United States
Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.
Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.
We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.
The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:
We are seeking an experienced Senior Network Engineer (InfiniBand / UFM)to design, deploy, automate, and operate next-generation AI Factory networking infrastructure supporting large-scale GPU clusters. This role is responsible for building and maintaining high-performance NVIDIA Quantum InfiniBand fabrics that power AI training and inference environments utilizing NVIDIA UFM, NCCL, RoCEv2, and modern data center technologies.
The ideal candidate has deep expertise in InfiniBand networking, NVIDIA Unified Fabric Manager (UFM), large-scale GPU deployments, Linux networking, automation, and troubleshooting distributed AI workloads.The network is the foundation of distributed AI training. Performance bottlenecks at the fabric layer directly impact customer workloads.This role will shape how Lightning scales from tens of thousands of GPUs to significantly beyond — ensuring deterministic performance, predictable scaling, and enterprise-grade reliability.If you want to architect infrastructure that powers frontier AI research, this is the role for you.
This role may be fully remote within the U.S., or hybrid out of one of our office hubs (NYC, SF, Seattle, or London), with occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.
We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits.
The anticipated annual base salary range for this role is:
$170,000 - $210,000 USD
We offer a comprehensive and competitive benefits package designed to support our employees’ health, well-being, and long-term success:
Benefits may vary by location, team, and role.
At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.
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Have you worked at a startup before? * Select...
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The location of this role is flexible. Please select all locations you are able to work a hybrid schedule from (2+ days per week) without relocation assistance. If you are only able to work remotely, select “Remote only.” *
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