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AfterQuery

LLM app development platform with observability

Machine Learning Engineer - Data Privacy & Anonymization

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USAIn-person

About the Job

Requirements

  • Three to six years of relevant experience.
  • Strong software engineering background and experience shipping production systems.
  • Experience building production-scale data pipelines and handling large volumes of data.
  • Applied natural language processing experience with named-entity recognition, sequence labeling, or information extraction on messy text; fraud or trust and safety detection work focused on recall for rare events also qualifies.
  • Experience with low-latency inference services, streaming pipelines, sidecars, or event systems.
  • Ability to work quickly in a high-ownership, fast-changing environment.
  • Care for quality, precision, and customer impact.

Responsibilities

  • Build detection models for personally identifiable information, protected health information, and quasi-identifiers across free text, logs, structured payloads, and code.
  • Own the transformation layer, including redaction, masking, pseudonymization, tokenization, and format-preserving encryption, selected by entity and policy.
  • Ship the runtime adaptor, including streaming inference, latency budgets, fail-open versus fail-closed semantics, and schema drift handling.
  • Build evaluation infrastructure with recall-weighted metrics and re-identification attacks against the company's own output.
  • Make anonymization policy configurable as customer and jurisdiction requirements diverge.
  • Own high-impact systems from early design through production deployment.

Desired Qualifications

  • Experience with HIPAA Safe Harbor or Expert Determination, GDPR pseudonymization, differential privacy, k-anonymity, synthetic data, tokenization vaults, or prior health-tech or fintech privacy work.

About the Company

AfterQuery

LLM app development platform with observability

AfterQuery provides a data platform for developers to build, evaluate, and monitor LLM-powered applications. It connects to enterprise data sources like Notion, Slack, and databases and prepares data for use in Retrieval-Augmented Generation (RAG). It offers evaluation tools to test models before deployment and observability to monitor performance, costs, and issues such as hallucinations and prompt drift in production. Its end-to-end, data-centric workflow targets production-ready AI solutions for enterprises, backed by YC.

Company size
201-500
Company stage
Series A
Total funding
$30.1M
Headquarters
San Francisco, CA
Founded
2024

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