Liquid Ai
Liquid Labs - Research Engineer
7mo ago
USAMiddleRemotemachine learningneural networkspython
Research Engineer role focusing on designing and implementing novel AI architectures and strategies at the intersection of research and engineering.
Responsibilities
- 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.
- While San Francisco and Boston are preferred, we are open to other locations in the United States.
Other
- 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.
- 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.