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AI Model Risk Tiering Framework Engineer

Noah Kovacs

Full-time · Job · Los_Angeles

About the role

We're building the risk classification layer that enterprise compliance teams use to decide how much scrutiny a model deserves before deployment. We publish our methodology internally and encourage the team to submit findings to governance conferences, so this role blends applied engineering with research rigor. You'll design the scoring framework that assigns models to risk tiers based on data sensitivity, decision impact and prior audit findings, then build the tooling that lets governance teams override and explain those scores. The tiers you define will gate which models are allowed to reach production across dozens of enterprise customers.

Responsibilities

  • Design risk-tiering methodology grounded in published governance research
  • Build scoring pipelines that classify models by regulatory exposure
  • Publish internal findings and represent the team at governance conferences
  • Partner with customer compliance teams to calibrate tier thresholds

Requirements

  • 5+ years in ML engineering with exposure to model governance
  • Strong statistics background
  • Experience translating research into production tooling
  • Comfortable presenting findings to non-technical stakeholders

Benefits

  • Fully remote, US time zones preferred
  • Conference travel budget
  • Competitive salary plus equity
  • Health, dental and vision coverage

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Categories

MLOps & AI Infrastructure AI Business, Marketing & Strategy

Skills

python statistics mlflow sql

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure AI Business, Marketing & Strategy

Skills

python statistics mlflow sql

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