Senior Product Manager, AI Infrastructure

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:

• 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.

We’re looking for a Product Manager, AI Infrastructure to own the product experience for Lightning AI’s GPU cloud, from capacity and provisioning through workload execution, reliability, observability, and customer consumption.

This role sits at the intersection of AI infrastructure, cloud platforms, and developer experience. You’ll define how customers discover, provision, configure, and operate GPU compute, while partnering closely with infrastructure engineering and operations to make the platform more reliable, efficient, and scalable.

The right candidate understands that infrastructure itself is the product. You should be comfortable reasoning about GPU availability and utilization, cluster provisioning, networking and storage, scheduling and orchestration, workload reliability, and the APIs and workflows that expose these systems to customers.

You’ll work across Engineering, Infrastructure, Sales, customers, and the executive team to determine what we build, how infrastructure capabilities are exposed and packaged, and where Lightning AI can differentiate from hyperscalers and other GPU clouds.

This is a high-ownership role. You’ll investigate infrastructure and customer problems directly, use data to understand reliability and utilization, make technical tradeoffs with engineers, and drive products from problem definition through launch, adoption, and iteration.

You’ll join the Product team and report to our VP of Product. This is a hybrid role based in New York City or San Francisco, with an in-office expectation of two days per week.

• →Own the product vision and roadmap for Lightning AI’s GPU cloud infrastructure.
• →Define how customers discover, provision, configure, and consume GPU compute.
• →Build product experiences around GPU capacity, clusters, scheduling, networking, storage, and workload execution.
• →Partner closely with infrastructure and platform engineering to improve availability, reliability, utilization, and performance.
• →Develop a deep understanding of customer workloads, from experimentation and training through inference, and translate those needs into infrastructure capabilities.
• →Use infrastructure and product data to identify capacity constraints, reliability issues, performance bottlenecks, and opportunities to improve the customer experience.
• →Define the APIs, interfaces, and developer workflows through which customers interact with infrastructure.
• →Make product tradeoffs across customer experience, infrastructure efficiency, reliability, cost, and engineering complexity.
• →Own pricing, packaging, and consumption models in partnership with Ops, Sales, and Finance.
• →Partner with GTM on positioning, technical sales conversations, customer feedback, and competitive differentiation.
• →Define and track metrics across GPU utilization, provisioning, workload reliability, infrastructure consumption, adoption, and retention.
• →Take products from problem discovery through requirements, launch, adoption, and iteration.

• 7+ years of product management experience, including 3+ years building cloud infrastructure, compute, platform, developer tooling, or AI infrastructure products.
• Experience building technical products for developers, infrastructure teams, ML engineers, AI researchers, or other technical users.
• Strong understanding of cloud infrastructure concepts including compute, networking, storage, provisioning, scheduling, and orchestration.
• Familiarity with GPU infrastructure and the requirements of large-scale AI training, experimentation, or inference workloads.
• Technical depth to work directly with engineers on APIs, distributed systems, Kubernetes, workload orchestration, observability, and infrastructure reliability.
• Experience using data to understand infrastructure utilization, capacity, reliability, performance, and customer behavior.
• Track record of owning technical products from problem definition through launch and adoption.
• Strong product judgment and ability to turn complex infrastructure capabilities into simple customer experiences.
• Experience with pricing, packaging, consumption-based products, or cloud infrastructure unit economics.
• Strong prioritization skills and comfort making tradeoffs across customer needs, reliability, infrastructure efficiency, and engineering investment.
• Strong written and verbal communication across technical, customer, and executive audiences.
• Comfortable moving quickly and operating in ambiguous environments.
• BS in Computer Science, Engineering, or equivalent practical experience.

• Experience at a GPU cloud, neocloud, hyperscaler, AI infrastructure company, or infrastructure developer-tools company.
• Experience building products involving GPU provisioning, cluster management, capacity management, workload scheduling, or distributed compute.
• Familiarity with GPUs, Kubernetes, Slurm, Ray, PyTorch, distributed training, or similar infrastructure technologies.
• Experience with reserved capacity, on-demand compute, utilization optimization, or other cloud consumption models.
• Experience building infrastructure products that support large-scale AI training and inference.
• Experience working closely with data center, hardware, networking, or infrastructure operations teams.