Infrastructure Engineer (GPU & Compute)
Lightning AI
| Company | Lightning AI |
| Category | Engineering |
| Location | London |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 1 May 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
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're Looking For
Lightning AI is seeking a GPU & Compute Infrastructure Engineer to join our Infrastructure Engineering team.
In this role, you will own image management, system diagnostics, and validation across large-scale bare-metal compute infrastructure, with a particular focus on GPU-enabled systems. You will work at the intersection of hardware, systems, and software—developing automation, improving reliability, and enabling efficient cluster bring-up for AI/ML and HPC workloads.
You will play a key role in owning and evolving our image pipeline, running validation environments and test clusters, and supporting both system-level and GPU hardware qualification. This role is critical to ensuring that our infrastructure is consistent, performant, and ready to support demanding AI workloads from day one.
This role is based in one of our hubs (NYC, SF, Seattle, or London), with a minimum of 2 in-office days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time.
What You'll Do
Systems, Image & Validation Infrastructure
Own and evolve systems for image management, deployment, and validation across bare-metal infrastructure
Run and maintain test clusters used for system validation, diagnostics, and bring-up
Validate firmware, drivers, and OS images across compute and GPU-enabled systems
Support hardware qualification efforts for next-generation platforms
GPU Diagnostics & Performance
Own GPU diagnostics and validation workflows across large-scale infrastructure
Diagnose and resolve complex issues across GPUs, drivers, OS, and hardware layers
Analyze system and GPU performance using tools such as NVIDIA DCGM
Identify failure patterns and drive improvements in system stability and validation coverage
Automation & Tooling
Build and maintain automation for provisioning, validation, and system bring-up
Develop Python-based tools and workflows to improve efficiency and reduce manual operational overhead
Improve the reliability, repeatability, a
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