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The day-to-day work here is designing, synthesizing, filtering, and mixing pre-training corpora as a research problem and running experiments to measure which data changes improve model behavior. This function as a dedicated research-engineer role is a recent specialization that barely existed before large foundation model training became the central bottleneck.
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:
Thinks like a researcher, ships like an engineer: You build the pipelines and you make the model-facing calls about what goes into them. You don't hand off either half.
Leads with a hypothesis: Before changing a dataset, you can say what it should do to the model, what you'll measure, and what could fool you, such as contamination or a noisy benchmark. You hold views on pre-training pitfalls, and you test them.
Owns problems end to end: You take ambiguous work from open question to shipped result with little direction or process, do the unglamorous parts that get it there, and don't care whose job it was.
Source web-scale and public datasets, and build the pipelines that filter, deduplicate, and decontaminate them
Design data mixtures and curricula for pre-training and midtraining, and decide what goes into each run
Build synthetic data generation systems using LLMs, structured prompting, and distillation to cover what web data can't
Run proxy experiments and ablations to measure what each data change does to model quality, and decide what scales up
Build evaluation sets you can trust, and keep data lineage clear enough that any training run can be traced and reproduced
What you'll need:
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.
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
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
Production data pipelines you built and owned, not only research prototypes
Solid ML fundamentals: you've trained, evaluated, and iterated on models in Python (PyTorch preferred)
3+ years of relevant experience with an M.S., 1+ year with a Ph.D., or 5+ years with a B.S.
Helpful, not required:
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
Upstream of every model: Pre-training data shapes every Liquid Foundation Model. On a small team, the mixtures, filters, and synthetic data you build go straight into the models we ship.
Compensation & Equity: Competitive base salary plus equity, so you share in what we build. Final offers reflect skills, experience, and location.
Time Off: Unlimited PTO, plus company-wide Refill Days, long weekends when the whole company rests together.
Health & Wellbeing: Comprehensive health coverage, with details varying by location.
Paid Leave: Paid leave for pregnancy, parenting, medical needs, and caring for family.
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