Member of Technical Staff - ML Research Engineer, Data

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

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.

We need someone who:

• 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

• 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