AI Feature Attribution Stability Analyzer

About the role

Responsibilities

• Design attribution stability measurement methodology across SHAP, LIME, and gradient-based methods
• Build production monitoring tracking explanation consistency across repeated queries and minor input perturbations
• Implement stability benchmarks comparing attribution methods on client model and data types
• Create stability threshold recommendations tied to regulatory adverse action consistency requirements
• Develop explanation quality reports surfacing instability patterns requiring model or feature attention
• Research improved attribution methods with stronger theoretical stability guarantees

Requirements

• 6+ years in ML engineering with explainable AI, model interpretability, or fairness experience
• Deep understanding of feature attribution methods and their theoretical properties
• Strong Python and statistical analysis skills
• Familiarity with regulatory explainability requirements in financial services or healthcare
• Research track record in interpretable ML preferred

Benefits

• Hybrid: 2 days NYC, 3 days remote
• Top-of-market compensation for XAI expertise
• Research conference and publication budget
• Comprehensive health benefits
• Equity participation

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