Member of Technical Staff - ML Research Engineer, Data
Cambridge, Massachusetts, United StatesAll locationsCambridge, Massachusetts, United StatesSan Francisco, United States Hybrid
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About the role
Our Data team powers Liquid Foundation Models across pre-training, vision, audio, and emerging modalities. Public data sources are plateauing. Model performance increasingly depends on purpose-built datasets. We need ML-minded engineers who can collect, filter, and synthesize high-quality data at scale. We treat data as a research problem, not an infrastructure problem. Our engineers run experiments, design ablations, and measure how data decisions move model quality. We will match you to the team where you can grow the fastest and have the most impact: pre-training, post-training RL, vision-language, audio, or multimodal. While San Francisco and Boston are preferred, we are open to other locations.
What you'll bring
- 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
- What Success Looks Like (Year One)
- You own a critical data pipeline end-to-end for one of our modalities
- You have built or improved data systems that measurably moved model performance
- You have identified and integrated at least one external dataset that moved the needle