At Liquid, we’re not just building AI models—we’re redefining the architecture of intelligence itself. Spun out of MIT, our mission is to build efficient AI systems at every scale. Our Liquid Foundation Models (LFMs) operate where others can’t: on-device, at the edge, under real-time constraints. We’re not iterating on old ideas—we’re architecting what comes next.
We believe great talent powers great technology. The Liquid team is a community of world-class engineers, researchers, and builders creating the next generation of AI. Whether you're helping shape model architectures, scaling our dev platforms, or enabling enterprise deployments—your work will directly shape the frontier of intelligent systems.
This Role Is For You If:You thrive in high-autonomy, high-context environments and know how to turn vague questions into concrete experiments
You’ve worked on foundation models beyond just LLMs, and you understand the nuances of designing for real-world signals and feedback
You’re comfortable creating new training setups, loss functions, or evaluation methods tailored to customer-specific metrics
You have deep experience with the PyTorch ecosystem (including distributed training and third-party libraries), and can move quickly from prototype to production-scale experiments
You’re energized by tight feedback loops with customers and believe that experimentation should be aligned with product objectives
You can think at multiple altitudes—from quick-turn tests to longer-term architecture bets—and know when to scale each
Own the end-to-end experimental process: from hypothesis generation to results analysis to iteration
Design and run structured experiments to evaluate model performance across customer-specific metrics
Develop tooling and workflows that let the team rapidly test hypotheses and scale promising directions
Work closely with research, infra, and customer teams to prioritize experimental goals and interpret results
Translate customer needs into actionable model improvements via principled experimentation
Contribute to building a culture of fast, reproducible, and product-aligned model research
Lead high-leverage experimentation at the intersection of foundational model research and real-world customer impact
Hands-on, research-driven role where you'll own the end-to-end lifecycle of designing, running, and analyzing experiments that push forward our hybrid model systems
Your work will directly shape how our models perform in customer contexts, with an emphasis on measurable impact over theoretical gains
You’ll collaborate closely with infra, modeling, and customer teams to ensure that experimental insights are actionable and aligned with product goals
Spun out of MIT CSAIL, we’re a foundation model company headquartered in Boston. Our mission is to build capable and efficient general-purpose AI systems at every scale—from phones and vehicles to enterprise servers and embedded chips. Our models are designed to run where others stall: on CPUs, with low latency, minimal memory, and maximum reliability. We’re already partnering with global enterprises across consumer electronics, automotive, life sciences, and financial services. And we’re just getting started.
Top Skills
What We Do
Our mission is to build capable and efficient general-purpose AI systems at every scale
