Privacy Research Engineer, Safeguards

Anthropic Anthropic · AI Frontier · San Francisco, CA · AI Research & Engineering

Research Engineer focused on privacy for large language models, developing and auditing privacy-preserving training algorithms and techniques, and ensuring responsible data handling.

What you'd actually do

  1. Lead our privacy analysis of frontier models, carefully auditing the use of data and ensuring safety throughout the process
  2. Develop privacy-first training algorithms and techniques
  3. Develop evaluation and auditing techniques to measure the privacy of training algorithms
  4. Work with a small, senior team of engineers and researchers to enact a forward-looking privacy policy
  5. Advocate on behalf of our users to ensure responsible handling of all data

Skills

Required

  • Python
  • ML frameworks like PyTorch or JAX
  • Large language models
  • Privacy-preserving techniques (e.g., differential privacy, k-anonymity, l-diversity, t-closeness)

Nice to have

  • Published papers on privacy-preserving ML
  • Prior experience training large language models (e.g., collecting training datasets, pre-training models, post-training models via fine-tuning and RL, running evaluations on trained models)
  • Prior experience developing tooling to support privacy-preserving ML (e.g., differential privacy in TF-Privacy or Opacus)

What the JD emphasized

  • privacy-preserving machine learning
  • shipping products and features
  • large language models
  • privacy-preserving techniques
  • fast-paced startup engineering teams
  • ambiguous technical problems
  • lead cross-functional security initiatives

Other signals

  • privacy-preserving techniques
  • auditing
  • evaluation techniques
  • frontier models