Sr. AI Product Manager

About the Role

This is the senior product role on a small, high-leverage product team. You'll own key pieces of the product line — from how we position it, to how we price it, to how the field sells it. You'll work closely with engineering and be the person Marketing and Field teams looks to for the answer to "why us, why now".

We need a product leader who lives close to customers and to revenue, who can hold a deeply technical conversation with multiple stakeholders - from technical buyers to executive, and who treats GTM as a first-class part of the product — not something that happens after the product is "done."

What You Will Do

• Growth strategy for AI — where we play, who we sell to, how we win, and the sequencing of bets that gets us there
• The customer and buyer motion — being in front of inference platform teams, data platform leads, and the buyers who decide. Joining strategic deals. Running the win/loss loop and feeding it back into strategy.
• Positioning and narrative — partnering with Marketing on how the AI stories land in market, what we publish, and how a first-time buyer encounters MinIO
• Field partnership — building the assets sellers actually use, supporting the deals that matter, making sure the field can run an inference-buyer or lakehouse-buyer conversation confidently
• Pricing and packaging — how AIStor and MemKV are sold, how the AI and Analytics motions are priced differently if they should be, how we capture value as workloads grow
• Competitive intelligence — knowing the AI training inference competitive landscape, and shaping how we differentiate
• Partner GTM for AI — the AI ecosystem is partner-heavy, and unlocking it is part of the role
• Influence on the product — through customer signal, pipeline data, and competitive insight you will be the most informed external voice in the room to influence direction.

Your Skills and Experience

• 8+ years in product management, with at least 3 years on infrastructure, data, or AI/ML products sold to technical buyers
• Demonstrated fluency in modern AI and analytics stacks — you don't need to have trained a foundation model, but you should be conversant in how training and inference pipelines actually consume storage, key tradeoffs and why it matters, and how data platform teams make build/buy decisions
• A track record of working closely with field teams on real revenue — you've sat in on deals, you've built enablement that sellers actually used, you've changed a deal's outcome with a well-timed product conversation
• Partnership with marketing on crafting compelling narratives, presented at external conferences, webinars
• Strong analytical instincts — you reach for data to test your hypotheses, not to confirm them
• The ability to write clearly and think rigorously in writing. Most of the work that compounds in this role is written
• Excellent partnership with engineering — you've earned engineers' trust before by being technically credible and by making prioritization defensible
• Real comfort with pricing and packaging decisions — if you've owned or co-owned them, not just commented on them, that's a big plus