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AI Benchmark Metric Sensitivity Analyzer

Sofia Garcia

Freelance · Job · New_York

About the role

The same model can score 73% or 81% on the same benchmark depending on whether you use greedy decoding or temperature 0.7 sampling, whether you zero-shot or few-shot, and whether you measure exact match or F1. Benchmark metrics are sensitive to evaluation choices in ways that practitioners often don't understand and papers often don't report. Sensitivity analysis — systematically measuring how benchmark results change across evaluation parameter choices — is how you distinguish robust findings from artifacts. We build model evaluation infrastructure used by research teams at 20 AI organizations. Benchmark metric sensitivity analysis is a capability our clients want for their own benchmark results: they want to know before publishing how sensitive their numbers are to reasonable parameter variations, so they can either report confidence intervals or justify why their specific evaluation choices are appropriate. This is a freelance role — 20-25 hours per week — helping build the sensitivity analysis tooling. You'll implement parameter sweep pipelines that run the same benchmark under varied evaluation conditions, compute sensitivity statistics showing which metrics and benchmarks have high versus low sensitivity to specific parameters, and generate the sensitivity reports that researchers include in their evaluation methodology sections.

Responsibilities

  • Build evaluation parameter sweep pipelines running benchmarks across varied decoding, prompt, and metric configurations
  • Implement sensitivity statistics measuring metric variance across parameter ranges
  • Create benchmark sensitivity reports comparing which evaluation choices drive result variation
  • Develop automated flagging of high-sensitivity benchmark results requiring additional reporting
  • Build comparison tooling showing how sensitivity patterns differ across benchmark types
  • Document sensitivity analysis methodology for inclusion in research evaluation sections

Requirements

  • 1+ year of Python development experience with ML or statistics familiarity
  • Basic understanding of LLM evaluation concepts: decoding strategies, prompting, metric types
  • Interest in ML evaluation, benchmark design, or research methodology
  • Attention to statistical accuracy in sensitivity computation
  • Reliable async communication for a freelance engagement

Benefits

  • Fully remote freelance — 20-25 hours per week
  • Flexible schedule
  • Competitive hourly rate
  • Exposure to ML evaluation research community
  • Potential to extend into full benchmark platform engineering

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Categories

AI Product & Strategy data science & analytics

Skills

python sql pandas statistics pytorch

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Job Type

Freelance

Level

Junior

Language

English

Salary Range

$60k – $80k / year

AI Expertise

AI Product & Strategy data science & analytics

Skills

python sql pandas statistics pytorch scikit-learn

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