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Boston, Massachusetts, United States
Hybrid
Research has been core to Liquid AI from the beginning.
Liquid Labs gives that work a formal home; an internal research accelerator driving fundamental breakthroughs in the science of building intelligent, personalized, and adaptive machines.
Our origins trace back to MIT CSAIL, where the foundational work on Liquid Neural Networks defined a new class of dynamical, efficient sequence-processing architectures. That research became the basis for Liquid Foundation Models (LFMs). Scalable, multimodal models built for real-world deployment in resource-constrained environments.
At Liquid Labs, we extend that lineage - pushing forward the frontier of efficient, adaptive intelligence through both fundamental research and practical engineering.
We work hand-in-hand with Liquid’s core foundation model and systems teams to translate theory into deployed capability — defining a new generation of intelligent systems that are both powerful and efficient.
As a Research Engineer, you’ll join a small, high-context team exploring the limits of adaptive intelligence. You’ll design and implement novel architectures, training methods, and inference strategies to redefine what efficient AI can do.
You’ll operate at the intersection of research and engineering — translating scientific ideas into working systems, publishing where it drives the field forward, and deploying where it changes what’s possible.
Work fluently in Python and frameworks such as PyTorch, JAX, or TensorFlow
Have experience in machine learning research or production-grade ML systems
Move fast from paper to prototype — curiosity backed by precision
Care about efficiency, scalability, and elegant system design as scientific principles
Value small, deep-technical teams where impact is immediate and measurable
Have a track record of publication in tier-1 venues (NeurIPS, ICML, ICLR, CVPR, ACL, or equivalent), demonstrating original contribution and research rigor
Liquid Labs reinforces our commitment to transparent, reproducible, open research.
We publish through technical reports, architectural deep dives, ablations, and model releases, advancing the broader science of efficient AI while translating breakthroughs into production-ready systems.
Liquid Labs is for researchers who build.
Those who care about lasting impact more than publication count, but who hold themselves to the same scientific standard.
We don’t chase benchmarks; we redefine them.
We move fast, think deeply, and measure success by the systems that endure.
There is no application deadline. We review candidates on a rolling basis.