Member of Technical Staff - ML Research Engineer, Pre-Training 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.

You'll join the Pre-Training Data team, which decides what Liquid Foundation Models learn from: which data we source, filter, synthesize, and mix, and in what order. Public data sources are plateauing, so model quality increasingly depends on purpose-built datasets and on knowing which data changes actually help.

We treat data as a research problem, not an infrastructure problem. You'll work alongside our pre-training researchers on a small team, run the experiments that settle data questions, and build the pipelines that carry the answers into training runs.

We need someone who:

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  Deep, hands-on pre-training data work you did yourself: web-scale sourcing and curation, synthetic data generation, mixing and curriculum design, deduplication and decontamination. This is where depth matters most.

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  The ability to reason from a data decision to its effect on training: how data interacts with model size, architecture, and training dynamics, why more data can hurt, and which changes matter as models scale

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  Experiments you designed to test data changes, such as baselines, scaling ladders, small-scale proxies, and held-out loss, with a clear account of what a change did and why

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  Production data pipelines you built and owned, not only research prototypes

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  Solid ML fundamentals: you've trained, evaluated, and iterated on models in Python (PyTorch preferred)

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  3+ years of relevant experience with an M.S., 1+ year with a Ph.D., or 5+ years with a B.S.

• Distillation, long-context, or architecture-specific data strategy

• Designing evaluation frameworks and benchmarks for pretrained models

• Data work across both pre-training and post-training

• Small, efficient models or on-device deployment

• Startup experience, or a track record of connecting model work to product needs

• You own a critical pre-training data pipeline end to end

• Data systems you built or improved have measurably moved model performance

• You've identified and integrated at least one external dataset that moved the needle