LiQUiD is the AI Powered Salon Assistant that pays for itself – ensuring every service is profitable, every client comes back, and every decision grows your money. Backed by Techstars.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Evidence you've connected a data change to a model outcome, through an experiment you designed and can explain, including what you'd do differently
- Strong Python, experience with large-scale data processing, and practical experience training or fine-tuning models (PyTorch preferred)
- Agency paired with judgment. For example, you spotted a problem nobody assigned, explained why it mattered, and fixed it. Or you killed your own approach when the data said so. Or you chose not to build something and saved the team time
- A clear account of your own contribution: what you decided, what tradeoff you made, and what changed because of you
- Pragmatism: you size experiments to the compute, time and team you have, and build just enough process
- Experience with small or efficient models and with how architecture changes should affect data strategy
More about this role
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.
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,...
Browse similar: AI jobs · AI startup jobs · Startup jobs · Remote jobs · San Francisco Bay Area