
We build efficient general-purpose AI at every scale.
Headquarters / Origin: MIT CSAIL spinoff based in Boston/Cambridge
Product focus: Efficient, low-latency foundation models (LFMs) and edge deployment platform (LEAP)
Notable funding: Raised $37.5M seed (Dec 2023) and $250M Series A (Dec 2024, led by AMD)
Founding team: Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus
Efficiency and deployment of foundation models for on-device and low-latency environments
2023
Artificial intelligence / Machine learning
$37.5M
Announced December 6, 2023
$250M
Announced December 13, 2024; reporting cited a post-money valuation of over $2 billion
“Mix of VC, corporate investors, and notable angels including OSS Capital, PagsGroup, Breyer Capital, Samsung Next, Bold Capital Partners, and individual angels such as Tom Preston-Werner and Naval Ravikant”
| Company |
|---|
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 This is a rare chance to sit at the intersection of frontier foundation models and real-world deployment. You’ll own applied post-training work end-to-end for some of the world’s largest enterprises, while still contributing directly to Liquid’s core model development. Unlike most roles that force a trade-off between customer impact and foundational work, this role gives you both: deep ownership over how models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid’s post-training stack. If you care about data quality, evaluation, and making models actually work in production, this is a chance to shape how applied AI is done at a foundation-model company.
What We're Looking For We need someone who:
The Work
Must-have Desired Experience
Nice-to-have
What Success Looks Like (Year One)
What We Offer