Skip to content
← Liquid AI

Member of Technical Staff - Evaluations

Liquid AI · San Francisco

Frontier labremote
Apply on the lab’s site ↗

At a glance

Highlights

  • Own internal eval suite end-to-end that drives post-training decisions.
  • Design held-out evals for agentic workflows, computer use, cybersecurity.
  • Build evals for small edge models and release open-source evals with papers.

What they offer

  • Competitive base salary plus equity
  • Unlimited PTO plus company-wide Refill Days
  • Comprehensive health coverage
  • Paid leave for pregnancy, parenting, medical and family care

Ideal candidate

Proactive, white-box evaluator who owns benchmarks end-to-end, digs into outputs before trusting scores, and cares about rigorous, reproducible evals for small edge models that ship within weeks.

Requirements

  • Strong eval design: turn capability descriptions into accurate, generalizable, hard-to-game evals
  • Prior evals built from scratch such as a benchmark, eval paper, or contributions to lm-eval-harness, lighteval, OLMES, HELM, Inspect
  • Fluent in eval fundamentals: contamination, saturation, judge bias, and variance
  • Hands-on experience running and managing evals at scale in a team setting
  • Debugs unexpected scores by reading individual model outputs before trusting numbers
  • Strong software engineering to own eval infrastructure and debug inference and infra failures

Lab profile

About Liquid AI

MIT CSAIL spinout building efficient general-purpose AI for cloud to edge.

What they build

  • Liquid Foundation Models
  • LFM2.5-Audio

Known for

  • Efficiency-first foundation models
  • Data center to on-device deployment
  • Low latency with minimal memory usage
  • Liquid Neural Networks architecture

Key facts

  • Spun out of MIT CSAIL
More about Liquid AI →
Apply on the lab’s site ↗

Posted · Last seen