Member of Technical Staff - Evaluations
Liquid AI · San Francisco
Frontier labremote
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
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