About Liquid AI
What is Liquid AI?
Liquid AI is a Boston-based foundation-model company spun out of MIT CSAIL in 2023. They build general-purpose AI systems optimised for compute-constrained environments — smartphones, laptops, automotive infotainment, embedded devices, on-device IoT. Their core innovation is a new family of model architectures (Liquid Neural Networks / state-space models) that deliver competitive accuracy at a fraction of the inference cost of transformer-based competitors.
Founded 2023 by MIT CSAIL researchers (CEO Ramin Hasani, Mathias Lechner, Daniela Rus and colleagues). Has raised $537M across Seed → Series B, including investments from AMD Ventures, OSS Capital and a large Series B in 2024. Partner customers span consumer electronics, automotive, life sciences, and financial services.
Where will I work?
Headquartered in Cambridge, Massachusetts (the MIT-adjacent address). Several roles are listed as Boston-based with Remote-eligibility — typical of an early-stage research-heavy AI company.
What is the Liquid AI team like?
~100 employees across Research & Engineering, applied ML, edge-inference optimisation, customer success, and corporate functions. Heavy on PhD-level ML research talent from MIT, Stanford, CMU and ex-DeepMind / OpenAI / Anthropic backgrounds. Recent expansion into Israel and EMEA.
Work-Life Balance
Verified verbatim on 15 of 18 currently-live Ashby JDs: "unlimited PTO plus company-wide Refill Days throughout the year." The "Refill Days" are quarterly company-wide rest days layered on top of the unlimited-PTO policy — a structural recharge in addition to individual time off.
Perks and Benefits
- Unlimited PTO — verbatim on 15/18 current JDs
- Company-wide Refill Days — quarterly recharge days for the whole company
- Comprehensive healthcare (US-based)
- Equity in a $537M-raised, late-Series-B AI company
- Hybrid / remote-eligible working
- Research conference budget typical of MIT-spinout culture
How to Apply
Apply via Liquid AI's Ashby careers board. 18 currently-live roles across Edge Inference Engineering, Post-Training (Applied), Research, Customer Success, and Operations.