Machine Learning Engineer, Data Privacy & Anonymization
Afterquery · San Francisco
About this role
## Machine Learning Engineer — Data Privacy & Anonymization ### About AfterQuery AfterQuery is an applied research lab building data solutions for foundation model development. We curate and deliver the best data to power the best models, working directly in the loop of how frontier AI systems improve. We’re based in San Francisco, backed by leading investors (including Y Combinator) and angels from top AI organizations. ### Why Apply - **Massive opportunity**: YC’s fastest unicorn, valued at **$3.2B** - **Founding impact**: Own and architect core infrastructure powering our platform - **Equity & growth**: Competitive salary and meaningful equity; shape the engineering org as we scale - **Strong team**: Work alongside world-class engineers and researchers ### Overview AfterQuery builds the data and evaluation systems that power frontier AI models. We’re hiring a **Machine Learning Engineer, Data Privacy & Anonymization** to build lasting systems that enable safe handling of sensitive customer data. You’ll own **anonymization layers** and **inline infrastructure** that detects identifying information in production data streams and transforms that data without destroying its usefulness. ### Responsibilities - Build **detection models** for **PII, PHI, and quasi-identifiers** across: - free text, logs, structured payloads, and code - Own the **transformation layer**: - redaction, masking, pseudonymization, tokenization, and **format-preserving encryption** (chosen per entity and policy) - Ship the **runtime adaptor**: - streaming inference, latency budgets, and **fail-open vs. fail-closed** semantics, schema drift - Build **evaluation infrastructure** with recall-weighted metrics and **re-identification attacks** against our own output - Make **anonymization policy** a configurable surface as customer/jurisdiction requirements diverge - Own high-impact systems from early design through **production deployment** ### Required Qualifications - **3–6 years** of relevant experience - Strong software engineering background with experience shipping **production systems** - Experience building **data pipelines** at production scale - Applied NLP experience (e.g., **NER**, sequence labeling, information extraction on messy text) - Fraud/trust & safety detection work where **recall on rare events** was the objective also counts - Experience with **low-latency inference services** (streaming pipelines, sidecars, or event systems) - Ability to move quickly in a high-ownership, fast-changing environment - Deep care for **quality, precision, and customer impact** ### Preferred Qualifications - HIPAA Safe Harbor or Expert Determination - GDPR pseudonymization, differential privacy, k-anonymity - Synthetic data, tokenization vaults - Prior health-tech/fintech privacy work ### Company Benefits (for eligible employees) - Health Insurance: Medical, Vision, Dental - 401(k) with Employer Match - Daily Meals: Daily UberEats stipend - Monthly Wellness stipend - Commute covered *AfterQuery is an equal opportunity employer. Employment decisions are made without regard to legally protected characteristics under applicable federal, state, or local law. Reasonable accommodations are available as required by law.*
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