Product Manager

The company is redefining autonomous cloud and AI infrastructure. We're on a mission to free DevOps and platform engineers from manual resource management so they can focus on innovation, not tuning resources. The results: maximized performance and a reduction of cloud costs by up to 80%.

As the category leader in Autonomous Cloud and AI Infrastructure Resource Management, we're trusted by leading enterprises including Adobe, Wiz, Epic Games, Northwestern Mutual, Coinbase, DocuSign, and Fortune 100 companies to autonomously manage their most critical production environments.

Backed by Insight Partners, Lightspeed Venture Partners, and other leading VCs with over $210M in funding, the company is the leading player in a massive and growing market. We are building the autonomous infrastructure management platform that will power the next decade of enterprise compute.

What You'll Be Doing

• Own a product area of our platform (for example GPU and AI workloads, node consolidation, or workload optimization) and its roadmap, with clear outcomes.

• Turn customer signals from Gong calls, field escalations, and live cluster data into well-scoped specs that engineers trust.

• Work with R&D on design and tradeoffs, with close attention to safety and risk in customers' production clusters.

• Lead discovery directly with platform, SRE, and FinOps teams to understand their workflows, pain points, and objections.

• Own the launch of what you ship: release checklist, docs, and field enablement, so Sales and CS know what to tell customers.

• Define and track the metrics that show whether what you shipped worked: adoption, automation coverage, realized savings, and how fast field asks are resolved.

• Conduct competitive research and work with the GTM team on positioning.

• Use AI tools and agents as part of how you work: research, analysis, specs, and small internal tools.

What You'll Bring

• 3+ years as a Product Manager on infrastructure, DevOps, cloud, or developer-platform products.

• Hands-on Kubernetes and cloud knowledge: you can read a manifest, explain requests, limits, and autoscaling, and reason about the risk of a change in production.

• A track record of shipping features customers adopted, with the numbers to show it.

• Comfort with data: you can write a query or dig into a dashboard yourself.

• Clear writing in English: your specs and customer-facing explanations hold up on their own.

• AI-native, or eager to be: you build small tools and automations instead of waiting for them.

• Nice to have: GPU or ML infrastructure, FinOps, or experience in a field-facing role (SE, TAM).