Research Engineer, AI/ML Systems

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

We're looking for a curious, adaptable Research Engineer who enjoys solving difficult technical problems and building across the AI stack to join our Research Engineering function here at Lightning.

This role is intentionally broad, with a primary focus on post-training models and the systems that support it. You’ll work across ML engineering, software engineering, and AI systems to improve how we develop, train, evaluate, and deploy models. As team priorities evolve, you’ll have opportunities to contribute across developer tooling, infrastructure, and platform capabilities that help researchers and customers develop, train, and deploy AI more effectively.

We're looking for someone who enjoys learning new technologies, working across multiple technical domains, and tackling whatever problems have the greatest impact. Strong software engineering fundamentals, curiosity, and a willingness to continuously learn are more important than already being an expert in every area of AI systems. If you've spent meaningful time building AI projects, experimenting with PyTorch, contributing to open source, reproducing research, or exploring new ideas because you're genuinely interested, we'd love to hear about it.

This role is hybrid with a minimum of 2 in-office days per week in San Francisco, Seattle, NYC, or London, with fully remote work considered for candidates outside of our office hub locations. All employees participate in occasional team and company offsites.

What You'll Do

Develop and post-train models, while building and improving the systems and workflows needed to run, evaluate, debug, and scale training workloads.

Build software, tooling, and platform capabilities that improve how researchers, developers, and customers develop, train, and deploy AI systems.

Contribute to Lightning’s open-source projects by building new features, improving existing functionality, and collaborating with the broader developer community.

Work across deep learning systems, developer tooling, backend services, and platform infrastructure to solve a wide variety of engineering challenges.

Collaborate directly with customers to understand real-world AI workloads, investigate technical challenges, and translate those learnings into reusable product and platform improvements.

Prototype new ideas, evaluate approaches, and turn successful experiments into production-quality software.