Liquid AI

Source web-scale and public datasets and build pipelines for filtering, deduplication, and decontamination.Design data mixtures and curricula for pre-training and midtraining and determine the contents of training runs.Build synthetic data generation systems using LLMs, structured prompting, and distillation.Run proxy experiments, scaling ladders, ablations, and held-out-loss evalua

Design and build benchmark suites covering inference performance, model quality, and knowledge evaluation across hardware targets.Run external partner verifications, compare solutions against benchmarks, identify gaps, and clearly communicate findings.Port models such as LFM2 across runtimes and frameworks and verify correctness end-to-end.Maintain and extend the inference engine la