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Research Engineer, Privacy and Anonymization

hud
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Hybrid (San Francisco, Singapore, Remote (Asia), Remote (North America))$100k - $230kFull-timePosted 4w ago

About the role

Research Engineer role focused on building privacy and anonymization systems for AI data, including detection/removal of PII and sensitive information, and end-to-end data pipelines to protect privacy without sacrificing data utility.

Responsibilities

  • Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information and design transformations based on the data type and downstream use case
  • Develop and benchmark detection approaches that combine rules, statistical models, classifiers, and LLM-based methods
  • Build production pipelines that anonymize raw data before it enters downstream processing, training, evaluation, or synthetic data generation workflows
  • Create evaluation frameworks that measure privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts
  • Design systems that remain robust to new data sources, schema drift, unusual formats, and sensitive information embedded in unexpected fields
  • Work with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards

Qualifications

  • Strong proficiency in Python and experience building reliable production data or ML systems
  • Experience with information extraction, named-entity recognition, classification, or related methods for detecting rare or sensitive content
  • Strong experimental instincts and the ability to compare approaches across recall, precision, latency, cost, and downstream data utility
  • An understanding of the difference between redaction, masking, pseudonymization, anonymization, and synthetic data—and when each is appropriate
  • High attention to detail and the ability to reason about subtle leakage paths, edge cases, and adversarial failure modes
  • Built data processing pipelines end-to-end without a fully prescribed roadmap

Skills

Required:PythonInformation extraction / NER / classificationPrivacy concepts (redaction, masking, pseudonymization, anonymization, synthetic data)Experimentation / evaluationRobustness to data drift / edge casesData pipelines
Last updated Oct 21, 2026. Interested? Apply on the company site.
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About hud

Platform for building RL environments and evals

HUD (YC W25) is developing agentic evals and RL environments for Computer Use Agents (CUAs) that browse the web for frontier AI labs. Our CUA Evals framework is the first comprehensive evaluation tool for CUAs. People don't actually know if AI agents are working reliably. To make AI agents work in the real world, we need detailed evals for a huge range of tasks. We're backed by Y Combinator, and work closely with frontier AI labs to provide agent evaluation and training infrastructure at scale.

artificial-intelligencereinforcement-learning
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