Member of Technical Staff
Liquid AI
Member of Technical Staff
Liquid AI
Provides generative, edge-capable foundation models
Role: Member of Technical Staff - Evaluations
Team: Post-Training
Location: San Francisco
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.
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.
We need someone who:
Proactive execution: You pick up use cases and come back with working benchmarks. You parallelize work, make progress visible, and finish without being chased.
White-box about evals: You volunteer where your own evals or scores were wrong and what you changed, and you present numbers with their caveats attached.
Work on the internal eval suite and infrastructure: keep it reliable, optimized, and reproducible at scale.
Design held-out evals for priority capabilities such as agentic workflows, computer use, and cybersecurity, with documented construction and contamination checks.
Build evals targeted at small edge models, where most public evaluations built for frontier models don't apply.
Read generated outputs to debug unexpected scores before anyone acts on them.
Turn customer and open-source feedback into environments usable for data, training, and evaluation, and partner with other teams to design evals for their specific needs.
Release open-source evals, write papers about them, and publish failure-mode reviews of popular evaluations.
What you'll need:
Eval design is where we need you to be unusually strong: you can take a high-level description of a capability and turn it into an evaluation that is accurate, general-purpose, separates good models from bad ones, and is hard to game.
Evals you have built before that we can recognize or check, and that hold up on coverage, reproducibility, contamination handling, and the signal they were meant to give. At least one built from scratch: a benchmark, an eval paper, or real contributions to lm-eval-harness, lighteval, OLMES, HELM, or Inspect.
Fluency with the fundamentals: contamination, saturation, judge bias, and variance.
Hands-on experience running and managing evals in a team, whether on an eval team, a training team, or in an engineering/DevOps role.
The habit of debugging unexpected scores by reading individual generated answers before believing the number.
Software engineering strong enough to own eval infrastructure and debug the inference and infra failures that surface when running evals at scale, without hand-holding.
Helpful, not required:
Model training experience.
Publications or research background (industry backgrounds are equally welcome).
Deep systems expertise.
Familiarity with what small edge models do and don't do well.
An eval-specific title is not needed: training-team or DevOps profiles who ran a lot of evals are a good fit.
Internal evals cover every main target capability and drive post-training decisions, so that model decisions in post-training cite an internal eval you built or hardened rather than an external benchmark.
The eval suite runs reliably at scale and is co-owned, with other teams coming to you for evals for their own models.
At least one open-source eval released with a paper, plus a failure-mode report on a popular evaluation, with Liquid's edge-model evals becoming an external reference.
Evals are the job: You work on the whole eval function alongside one engineer, not one benchmark inside a large eval org, with real compute to run evals at scale and a hiring manager with a large public following who wants to open-source and publish the work.
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.
Liquid AI builds and deploys foundation models based on liquid neural networks, focusing on efficient, on-device AI. It develops Liquid Foundation Models (LFMs), a family of generative AI models designed to be smaller and more computation-efficient than typical large language models, enabling deployment on edge devices with lower latency, better privacy, and reduced infrastructure costs. The approach includes end-to-end AI expertise and customizable architectures for enterprises that require real-time performance and private processing. Compared to traditional AI providers, Liquid AI emphasizes edge-ready, adaptable models that run efficiently on constrained hardware, and it targets enterprise-grade, private, reliable AI solutions. The company’s goal is to enable real-time, on-device AI at scale for businesses by offering compact, capable foundation models and the tools to customize them for specific applications.
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