About Liquid AI

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

The Opportunity

We're hiring a Product Manager to drive specific product bets from idea to product-market fit.

This is a hands-on, ground-level role: you own an experiment, or a small set of them, get the product into real users' hands, and iterate toward PMF, working shoulder to shoulder with our ML, inference, and engineering teams to turn their energy into crisp, executable direction.

It is not a traditional SaaS PM role: the model and the product are inseparable, our core offering is customization & fine-tuning rather than prompt-only use, and real model depth is a prerequisite.

What We're Looking For

We need someone who:

• Focus-giver: You turn high-energy, unfocused ideas into tight, executable product direction that engineers can build against.
• Evidence over intuition: You put products in front of real users early and let honest feedback drive decisions.
• Technically fluent and AI-native: You hold your own with ML and inference engineers, know when an LFM is the right tool, and use AI & coding tools to prototype and test hypotheses yourself.
• Conviction without stubbornness: You hold a clear point of view, update it in light of evidence, and can hit the ground running with light support.

The Work

• Own one or more active product experiments end-to-end and drive them toward product-market fit.
• Turn vague, high-energy ideas into tight, executable product requirements that engineers can build against.
• Put the product in real users' hands early, gather genuine feedback, and iterate.
• Ruthlessly prioritize: decide what to build now, what to defer, and what to kill.
• Work closely with ML, inference, and engineering teams, and flex across bets as priorities shift.

Desired Experience

Must-have:

• Direct, hands-on ML or applied LLM experience: you have built or trained models, you know the difference between prompting and fine-tuning in practice, and you know when an LFM is the right tool.