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San Francisco, California, United States
Onsite
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.
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.
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.
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.
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.
A strong bias for action: you use AI and coding tools to prototype and test hypotheses yourself, so your insights are higher-signal than a PM who ran deep research once.
Demonstrated ownership of product direction and prioritization, not just execution against a handed-down roadmap.
The ability to turn an ambiguous idea into a concrete, buildable plan, with light support rather than heavy coaching.
Nice-to-have:
Enterprise product expertise: the focus and execution it takes to bring a new enterprise product to market and win in enterprise.
Experience at an AI/ML company or on AI-powered products.
Familiarity with edge or on-device inference, agentic harnesses, evals, or observability.
You own a bet end to end and take it from an ambiguous opportunity to a validated, or confidently killed, product direction backed by real user evidence.
The engineers you support move faster because your requirements are crisp and well-prioritized.
You have established a repeatable way to get products in front of users and turn their feedback into decisions.
Focused ownership: You drive a product bet end to end rather than owning a sliver of a large platform.
Compensation: Competitive base salary with equity in a unicorn-stage company.
Health: We pay 100% of medical, dental, and vision premiums for employees and dependents.
Financial: 401(k) matching up to 4% of base pay.
Time Off: Unlimited PTO plus company-wide Refill Days throughout the year.