Machine Learning Engineer, Data Privacy & Anonymization

Machine Learning Engineer, Data Privacy & AnonymizationAfterQuery builds the data and evaluation systems that power frontier AI models. Every leading AI lab uses our datasets and reinforcement learning environments to encode and scale real-world expertise.We're hiring a Machine Learning Engineer, Data Privacy & Anonymization to build the lasting systems that enable us to handle sensitive customer data safely. You'll own anonymization layers, and infrastructure that sits inline with production software, detects identifying information in whatever passes through it, and transforms that data without destroying its usefulness. You'll support AfterQuery's mission by curating trainable data from real-world corpuses.ResponsibilitiesBuild detection models for PII, PHI, and quasi-identifiers across free text, logs, structured payloads, and codeOwn the transformation layer: redaction, masking, pseudonymization, tokenization, and format-preserving encryption, chosen per entity and policyShip the runtime adaptor: streaming inference, latency budgets, fail-open vs. fail-closed semantics, schema driftBuild evaluation infrastructure with recall-weighted metrics and re-identification attacks against our own outputMake anonymization policy a config surface as customer and jurisdiction requirements divergeOwn high-impact systems from early design through production deploymentRequired Qualifications3-6 YOE with relevant experiencesStrong software engineering background with experience shipping production systemsExperience building data pipelines at production scale, handling large volumes of dataApplied NLP: NER, sequence labeling, or information extraction on messy text. Fraud or trust/safety detection work where recall on rare events was the objective also countsExperience with low-latency inference services: streaming pipelines, sidecars, or event systemsAbility to move quickly in a high-ownership, fast-changing environmentDeep care for quality, precision, and customer impactPreferred QualificationsHIPAA Safe Harbor or Expert Determination, GDPR pseudonymization, differential privacy, k-anonymity, synthetic data, tokenization vaults, or prior health-tech/fintech privacy work.Company Benefits (For Eligible Employees):Health Insurance: Medical, Vision, Dental401(k) with Employer MatchDaily Meals: Daily UberEats StipendMonthly Wellness StipendCommute CoveredWe are an equal opportunity employer committed to providing a workplace free from discrimination and harassment. Employment decisions are made without regard to legally protected characteristics under applicable federal, state, or local law.We comply with applicable pay transparency requirements and provide compensation ranges based on the position, qualifications, experience, and other relevant factors. Reasonable accommodations are available to qualified individuals with disabilities and for sincerely held religious beliefs, as required by law. This job description is intended to describe the general nature and level of work performed and is not an exhaustive list of all duties, responsibilities, qualifications, or working conditions associated with the position. We reserve the right to modify this job description as business needs change.