MIT · AI
Role: Member of Technical Staff - Evaluations
Team: Post-Training
Location: San Francisco
Location Type: Hybrid
Employment Type: Full-time
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
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.
WHAT WE'RE LOOKING FOR
We need someone who
- Proactive execution: You pick up use cases and come back with working benchmarks. You parallelize work,