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AI Underwriting Risk Explainability Engineer

Xavier Tanaka

Full-time · Job · New_York

About the role

PyTorch, MLflow, and a decade of underwriting data are the core of what we build. Our risk models decide pricing for thousands of commercial policies every week, and regulators increasingly demand that every score come with a clear, auditable explanation. You'll design the explainability layer that sits on top of our production risk models, translating feature attributions into language underwriters and auditors can actually act on. This isn't a bolt-on reporting tool — it becomes part of the decision path itself, gating any score that can't be explained with confidence. If you'd rather ship something regulators trust than chase another leaderboard metric, this role is built for you.

Responsibilities

  • Own the full explainability pipeline from model output to underwriter-facing report
  • Partner with product to define what counts as an acceptable explanation for a denied claim
  • Build regression tests that catch explanation drift when models are retrained
  • Work directly with compliance to keep audit trails defensible under regulatory review

Requirements

  • Production experience with PyTorch and MLflow model lifecycle tooling
  • Comfort reading and explaining gradient-based and SHAP-style attribution methods
  • Experience shipping ML systems in a regulated industry (insurance, banking, healthcare)
  • Working knowledge of Docker-based deployment on AWS

Benefits

  • Fully remote position with quarterly team offsites
  • Equity grant reviewed annually
  • Health, dental, and vision coverage from day one
  • $2,000 annual learning stipend

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Categories

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch mlflow aws docker

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch mlflow aws docker

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