Liquid AI, San Francisco
Public benchmarks saturate and don't generalize to real-world tasks, so post-training risks overfitting to external scores. Every post-training decision at Liquid AI needs internal evals we trust: evals that track real-world behavior and are cheap and reproducible to run. This role sits in the Post-Training team and exists to make sure those decisions rest on exactly that kind of evidence.
You will work on the internal eval suite and infrastructure end to end working closely with the post-training researchers whose decisions depend on it and with other teams (audio and beyond) who need evals for their own models. Evaluating small edge models is an open problem, since nearly every public eval targets frontier models, and the evals you build here change the quality and capabilities of models we ship within weeks, not half a year.
Team: Post-Training
Location Type: Hybrid
Employment Type: Full-time
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.
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Source: the Liquid AI Ashby job board. We last checked this job 3 h ago. We first saw it 4 days ago. A job on a job board does not always mean that the team interviews people now. We found this job board in a public list. We did not yet compare it with the careers page of the company.
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