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AI Differential Privacy Engineer

Savannah Watanabe

Full-time · Job · New_York

About the role

Synthetic data is only as useful as the privacy guarantees it provides. If a synthetic dataset leaks information about the individuals it was generated from, it is not private — it is just noisy. Differential privacy gives you formal, provable guarantees about what a synthetic dataset can and cannot reveal, but implementing DP correctly is subtle and implementing it efficiently enough for real-world use is genuinely hard. We've built the infrastructure for generating synthetic tabular, time series, and text data at scale. The privacy math works. What we need now is someone who can push the utility-privacy frontier: better noise calibration that sacrifices less utility per unit of privacy budget, membership inference testing frameworks that give clients evidence of privacy protection, and tooling that helps data scientists work with DP concepts without needing a PhD in statistics. This is a research-engineering role. We publish our methods and engage with the academic DP community. You'll need to read and implement recent papers, and you may write some.

Responsibilities

  • Improve noise calibration mechanisms to reduce utility loss per privacy budget unit
  • Build membership inference attack frameworks for privacy guarantee testing
  • Develop tooling for data scientists to work with DP concepts intuitively
  • Implement DP-SGD and related techniques for differentially private ML training
  • Benchmark privacy-utility trade-offs against academic baselines
  • Engage with the DP research community through publications and open source

Requirements

  • 5+ years in ML engineering with privacy, security, or statistics specialization
  • Deep understanding of differential privacy theory and implementation
  • Experience with DP-SGD, the moments accountant, or related techniques
  • Strong Python and PyTorch skills
  • Publications or significant contributions to privacy-preserving ML research preferred

Benefits

  • Fully remote
  • Research conference and publication support
  • Top-of-market salary
  • 30 days PTO
  • Equity with 4-year vesting

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch statistics docker aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch statistics docker aws

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