Machine Learning Engineer, Data Privacy & Anonymization

AfterQuery is an applied research lab curating data solutions for foundation model development. We serve every frontier AI lab with the mission of delivering the best data to power the best models. In doing so, we can make expertise that once took a lifetime to build available to anyone who needs it.

Our customers are the ones building the foundation models themselves and our work sits directly in the loop of how those systems improve. This is a rare opportunity to join a company at a defining moment in AI. Forbes reported that we could be YC's fastest unicorn, reportedly raising at a $3.2 billion valuation. We're based in San Francisco and backed by leading investors including Altos Ventures, BoxGroup, and Y Combinator and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI.

Massive Opportunity: Forbes reported that we could be YC's fastest unicorn, reportedly raising at a $3.2 billion valuation, and we're not slowing down.

Founding Impact: You will own and architect core infrastructure systems that power our platform from the ground up.

Equity & Growth: Competitive salary and meaningful equity. As we scale, you’ll have the opportunity to shape the engineering organization and lead major technical initiatives.

Strong Team: Our founding team has experience from Citadel Securities, Meta, Google, Silver Lake, and Morgan Stanley — work alongside world-class engineers and researchers.

AfterQuery 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.

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  Build detection models for PII, PHI, and quasi-identifiers across free text, logs, structured payloads, and code

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  Own the transformation layer: redaction, masking, pseudonymization, tokenization, and format-preserving encryption, chosen per entity and policy

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  Ship the runtime adaptor: streaming inference, latency budgets, fail-open vs. fail-closed semantics, schema drift

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  Build evaluation infrastructure with recall-weighted metrics and re-identification attacks against our own output

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  Make anonymization policy a config surface as customer and jurisdiction requirements diverge

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  Own high-impact systems from early design through production deployment

• 3-6 YOE with relevant experiences

• Strong software engineering background with experience shipping production systems

• Experience building data pipelines at production scale, handling large volumes of data

• Applied 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 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

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