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AI Explainability Toolkit Developer

Noah Kovacs

Full-time · Job · New_York

About the role

SHAP values are great for tabular models. They're useless for explaining why a 70-layer transformer rejected a loan application to a compliance officer who doesn't know what a gradient is. The explainability gap between what researchers publish and what practitioners need is enormous — and we're building the toolkit to close it. Our platform provides model-agnostic explanations tailored to different audiences: technical explanations for ML engineers debugging models, compliance explanations for auditors validating decisions, and plain-language explanations for end users who deserve to know why an AI system affected their lives. Each audience needs different granularity, vocabulary, and framing. You'll design the explanation generation engine: the methods that extract decision factors from black-box models, the templates that present those factors in audience-appropriate formats, and the evaluation metrics that measure whether explanations are actually faithful to model behavior. The responsible AI team is six people; you'll be the senior engineer driving the technical roadmap.

Responsibilities

  • Design model-agnostic explanation methods for diverse model architectures
  • Build audience-specific explanation templates: technical, compliance, end-user
  • Develop evaluation metrics for explanation faithfulness and usefulness
  • Create the explanation generation API used by client applications
  • Research and implement new explainability techniques from the literature
  • Set technical direction for the explainability product roadmap

Requirements

  • 6+ years in ML with deep focus on interpretability and explainability
  • Experience with SHAP, LIME, attention analysis, and concept-based explanations
  • Strong Python and PyTorch skills for method implementation
  • Ability to translate technical concepts for non-technical audiences
  • Publications or significant contributions in explainable AI preferred

Benefits

  • Fully remote with async culture
  • Top-tier compensation plus equity
  • Comprehensive health coverage
  • Unlimited conference and research budget
  • 20% time for independent research

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Categories

AI Product & Strategy AI & Machine Learning Engineers

Skills

python pytorch scikit-learn docker fastapi

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI Product & Strategy AI & Machine Learning Engineers

Skills

python pytorch scikit-learn docker fastapi statistics

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