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

Liquid AI; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Unlimited PTO and Refill Days

Equity opportunities

Comprehensive health coverage

Paid leave for family and medical

Job summary

Liquid AI, spun out of MIT CSAIL, is seeking a data-centric ML engineer in San Francisco to own end-to-end pre-training data pipelines. You will source, filter, and synthesize data for foundation model training, building pipelines that scale with demands of large models.

You will run experiments to quantify data changes, reason about data interactions with model size and architecture, and ensure reproducible training runs. We value hands-on data work and a track record of shipping data solutions.

Qualifications

• Deep, hands-on pre-training data work you did yourself.
• Ability to reason from a data decision to its effect on training.
• Experiments you designed to test data changes with baselines, scaling ladders, and held-out loss.

Responsibilities

• Source web-scale and public datasets and build pipelines to 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.
• Run proxy experiments and ablations to measure data changes and how they affect model quality.
• Build evaluation sets and maintain data lineage for train/test reproducibility.

Skills

Pre-training data work

Data impact reasoning

Experiment design

Production data pipelines

Python

PyTorch

Education

MS in ML/CS

PhD in ML/CS

BS with 5+ years experience

Tools

PyTorch

Python

Job description

About Liquid AI

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.

About Liquid AI

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.

The Opportunity

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.

What We're Looking For

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.

The Work

• 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

Experience & Capabilities

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

What Success Looks Like (Year One)

• 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

What We Offer

• 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.