AI Safety Research Engineer
Li Bearden
I build measurement infrastructure for AI safety.
LLM evaluation methodology and sycophancy reproducibility, grounded in 5 years of applied ML at Deepgram.
The work
Research
Run-level reproducibility of published sycophancy benchmarks — whether single-run numbers survive being re-run. PARROT reproduces cleanly as the positive control (Spearman 0.95–0.99); SycEval is the open falsification test.
Consulting
The LLM Evaluation Audit — applied evaluation engagements for teams shipping models to production. Eval design, reproducibility checks, and the infrastructure to run them.
PACT ↗ fieldbuilding
Public Alignment & Control Tooling — open-source research infrastructure, diagnostic tooling and practice environments for independent AI safety researchers.
Writing
Working notes and essays on evaluation methodology, eval validity, and the institutional dynamics of frontier AI safety review.