Labs - Research Engineer
Liquid AI · Boston
CHECKED 23H AGO
Frontier labremote
At a glance
Highlights
- Design novel architectures and training methods for efficient adaptive AI.
- Translate breakthroughs in Liquid Foundation Models into deployed systems.
- Explore the limits of adaptive intelligence with the Liquid Labs team.
Ideal candidate
A fast-moving researcher-builder who thrives in small deep-technical teams and cares about efficiency and elegant design as scientific principles, motivated by lasting impact over publication count.
Requirements
- Work fluently in Python and frameworks such as PyTorch, JAX, or TensorFlow
- Experience in machine learning research or production-grade ML systems
- Move fast from paper to prototype with curiosity and precision
- Care about efficiency, scalability, and elegant system design
- Value small, deep-technical teams with immediate, measurable impact
- Track record of publication in tier-1 venues (NeurIPS, ICML, ICLR, CVPR, ACL, or equivalent)
- hiring process
- No application deadline; candidates reviewed on a rolling basis.
Lab profile
About Liquid AI
MIT CSAIL spinout building efficient general-purpose AI for cloud to edge.
What they build
- Liquid Foundation Models
- LFM2.5-Audio
Known for
- Efficiency-first foundation models
- Data center to on-device deployment
- Low latency with minimal memory usage
- Liquid Neural Networks architecture
Key facts
- Spun out of MIT CSAIL