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AI Privacy Engineering Lead

Leo Larsson

Full-time · Job · Los_Angeles

About the role

Training ML models on sensitive data without exposing individual records is the defining challenge of enterprise AI adoption. Our privacy engineering platform enables federated learning, differential privacy guarantees, and secure computation — so clients can train models on data they're legally prohibited from centralizing. As privacy engineering lead, you'll own the technical stack: federated learning orchestration across client data silos, differential privacy mechanisms calibrated to utility-privacy tradeoffs, secure aggregation protocols, and the privacy budget management system that tracks cumulative information leakage across queries. This is a rare role that requires both ML engineering depth and cryptographic protocol understanding. You'll work with legal teams who define privacy requirements, ML teams who need usable models, and security teams who audit your implementations. The constraint landscape is complex, but the organizations that solve privacy-preserving ML unlock datasets their competitors can't touch.

Responsibilities

  • Design federated learning infrastructure across distributed client data silos
  • Implement differential privacy mechanisms with calibrated utility-privacy tradeoffs
  • Build privacy budget management tracking cumulative information leakage
  • Develop secure aggregation protocols for cross-organizational model training
  • Lead privacy engineering reviews with legal and security audit teams

Requirements

  • 6+ years in ML engineering with privacy or security focus
  • Deep understanding of differential privacy and federated learning
  • Experience with secure computation or cryptographic protocols
  • Kubernetes and Docker skills for distributed system orchestration
  • Ability to communicate privacy guarantees to non-technical stakeholders

Benefits

  • Fully remote — US time zones
  • Senior equity package
  • $4,000 annual conference and research budget
  • Pioneering privacy-preserving ML at enterprise scale

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Categories

MLOps & AI Infrastructure

Skills

python docker kubernetes aws sql

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python docker kubernetes aws sql statistics

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