Machine Learning Engineer, Data Privacy & Anonymization
San Francisco · San Francisco, California, United States
- Pay
- Pay not listedSimilar roles state $165K–$240K
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Engineering — team source
Department: Engineering
From the employer’s posting
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
Will you now or in the future require sponsorship for employment visa status (e.g., H-1B visa status)?
From the employer’s application form
What you’ll work on
Full postingBuild detection models for PII, PHI, and quasi-identifiers across text, logs, payloads, and code
Own the transformation layer for redaction, masking, pseudonymization, and tokenization
Build the runtime adaptor with streaming inference and latency budgets
Build evaluation with recall-weighted metrics and re-identification attacks
Make anonymization policy a configurable surface for customer and jurisdiction rules
From the employer’s posting
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, 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 config surface as customer and jurisdiction requirements diverge
What you’ll bring
All qualificationsCore experience
- 3-6 YOE with relevant experiences
- Experience building data pipelines at production scale, handling large volumes of data
- Experience with low-latency inference services: streaming pipelines, sidecars, or event systems
- Ability to move quickly in a high-ownership, fast-changing environment
Preferred experience
- 401(k) with Employer Match
Qualification wording
3-6 YOE with relevant experiences
Experience building data pipelines at production scale, handling large volumes of data
Experience with low-latency inference services: streaming pipelines, sidecars, or event systems
Ability to move quickly in a high-ownership, fast-changing environment
401(k) with Employer Match
About Afterquery
We serve every frontier AI lab with the mission of delivering the best data to power the best models.
In the employer’s words · Read in context
Posting record
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- Live
- First seen by us
- Aug 12, 2026 · 78 days ago
- Recorded sightings
- 98
- Last seen by us
- Oct 29, 2026
- Employer says posted
- Jul 11, 2026
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Job description
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.
Why ApplyMassive 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.
OverviewAfterQuery 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 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, 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 config surface as customer and jurisdiction requirements diverge
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.
Company Benefits (For Eligible Employees):
Health Insurance: Medical, Vision, Dental
401(k) with Employer Match
Daily Meals: Daily UberEats Stipend
Monthly Wellness Stipend
We 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.
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San Francisco, California, United States
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