Key skills
PythonMachine learning fundamentalsPyTorchSynthetic data generationData curationML evaluationLarge language models (LLMs)Vision-language models (VLMs)Computer vision data pipelinesAudio data pipelinesAIMachine LearningMLComputer Vision
About this role
Liquid AI is a company that builds general-purpose AI systems and is looking for a Machine Learning Research Engineer to join their Data team. The role involves collecting, filtering, and synthesizing high-quality data at scale while collaborating closely with various teams to enhance model performance.
Responsibilities:
- Build and maintain data processing, filtering, and selection pipelines at scale
- Create pipelines for pretraining, midtraining, SFT, and preference optimization datasets
- Design synthetic data generation systems using LLMs, structured prompting, and domain-specific generators
- Design and run evaluations and ablations to measure dataset's impact on model performance
- Monitor public datasets across text, vision, and audio domains
- Collaborate with pre-training, vision, and audio teams on modality-specific data needs
Requirements:
- Strong Python skills with the ability to quickly comprehend problems and translate them into clean, working code
- Solid ML fundamentals: experience training, evaluating, and iterating on models (PyTorch preferred)
- Track record of learning new technical domains quickly
- 3+ years relevant experience with an M.S., or 1+ year with a Ph.D. (5+ years with a B.S.)
- Experience with synthetic data generation, data curation, or ML evaluation (designing evals, benchmarking, measuring data and model quality)
- Experience with LLMs, VLMs, computer vision, or audio data pipelines
- Open-source contributions or publications at NeurIPS, ICML, ICLR, or CVPR