Who We Are

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 Way We Work

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:

Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.
Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.
Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.
Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.
Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.
Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.

What We Are Looking For

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.

What You’ll Do

Design, deploy, and maintain large-scale NVIDIA InfiniBand fabrics supporting AI/ML GPU clusters.
Deploy and administer NVIDIA Unified Fabric Manager (UFM) Enterprise for monitoring, provisioning, telemetry, and fabric health.
Configure and optimize NVIDIA Quantum and Quantum-2 InfiniBand switches.
Troubleshoot fabric performance issues impacting NCCL, MPI, GPUDirect RDMA, and AI training jobs.
Implement and validate fat-tree, Dragonfly+, Clos, and spine-leaf network architectures.
Perform firmware lifecycle management for InfiniBand switches, adapters (HCAs), and UFM infrastructure.
Optimize congestion control, adaptive routing, QoS, and traffic engineering for high-performance GPU communication.
Work closely with AI platform, GPU infrastructure, storage, and systems engineering teams to deploy scalable AI Factory environments.
Automate network provisioning using Python, Ansible, Git, REST APIs, and Infrastructure-as-Code methodologies.