Senior Product Manager, Experimentation Tooling
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 Senior Product Manager to own Lightning AI’s experimentation and post-training product end to end—from product strategy and roadmap through launch, adoption, pricing, and go-to-market. This is a role focused on how AI researchers and engineers turn an idea into a high-quality, validated model. You’ll define the workflow for running and comparing experiments, managing fine-tuning and reinforcement-learning workloads, evaluating model quality, understanding failures, and selecting the right model or checkpoint for production. You’ll work at the intersection of developer tooling, AI infrastructure, and model quality. The right candidate understands how modern AI teams work today: notebooks, training jobs, experiment trackers, checkpoints, evaluation suites, spreadsheets, and custom internal tooling. You can identify where those workflows break down and turn them into a cohesive, minimal product experience. You should be able to move fluidly between designing an intuitive developer workflow, discussing distributed training and artifact lineage with engineers, evaluating model outputs, and explaining the product’s value to...