ML Pre-Training Data Scientist & Pipeline Engineer

AItoolnavio; San Francisco (CA); On-site; USD 150,000 - 220,000; Full time

Benefits offered by this job

Unlimited PTO

Equity

Health coverage

Paid leave

Job summary

Liquid AI in San Francisco is seeking an experienced data-focused ML professional to own pre-training data pipelines end-to-end. You will source web-scale datasets, design mixtures, build synthetic data systems with LLMs, and run proxy experiments to measure impact on model quality.

Production pipelines ownership and reproducibility are essential. Strong background in Python and PyTorch, plus 3+ years with an MS or 5+ years with a BS, are highly valued.

Qualifications

• 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.
• 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.

Responsibilities

• 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.

Skills

Pre-training data work

Data pipeline engineering

Python (PyTorch)

Experiment design

Model training & evaluation

Data analysis

Education

M.S. in a relevant field

Ph.D.

B.S.

Job description

Liquid AI in San Francisco is seeking an experienced data-focused ML professional to own pre-training data pipelines end-to-end. You will source web-scale datasets, design mixtures, build synthetic data systems with LLMs, and run proxy experiments to measure impact on model quality.

Production pipelines ownership and reproducibility are essential. Strong background in Python and PyTorch, plus 3+ years with an MS or 5+ years with a BS, are highly valued.