ICML 2026
remote
via Ashby
About this role
ABOUT LIQUID AI
Spun out of MIT CSAIL, we build foundation models from scratch using a fundamentally different architecture. Our Liquid Foundation Models (LFMs) are built on a fundamentally different hybrid architecture; they deliver faster inference, lower memory, and deploy where traditional models can't. We ship open-weight text, vision-language, and audio-language models that run on phones, laptops, vehicles, and embedded devices.
WHY WE'RE AT ICML
We're here because ICML brings together the people working on the problems we care most about: efficient architectures, representation learning, multimodal reasoning, and the science of how models learn. If our conversation at the booth was interesting, this is the next step.
WHAT WE'RE BUILDING…
What we'd score you on
reqspace match rubricFive dimensions, recruiter-grade. Upload your resume and we'll generate a written explanation of where you fit and where the gaps are.
1
Skills match
For this role: github
2
Level fit
We check your title trajectory against the seniority signal of the role.
3
Domain experience
Your work in the role's domain matters more than your years total. We weight recent and direct experience.
4
Recency
A skill you used last quarter weighs more than one from five years ago. We grade on recency, not lifetime.
5
Location fit
This role is remote-eligible — we factor in your stated location and time-zone overlap.
Score yourself on this role.
Free · no card · written explanation included
Skills in this role
Pulled from the job description. These are the keywords we'll weight when scoring your fit.
github