Member of Technical Staff - ML Research Engineer, Data

Liquid AI builds general-purpose AI systems designed for efficient deployment across data centers and on-device hardware. The ML Research Engineer will develop and evaluate large-scale data pipelines, synthetic data systems, and dataset strategies for pretraining, post-training, vision, audio, and multimodal foundation models.

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.)
• While San Francisco and Boston are preferred, we are open to other locations
• 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