Machine Learning / Data Engineer

Run deep dives into enterprise data to assess quality: topic coherence across connectors, domain depth within connectors, completeness, and consistency.

Design and automate validation suites for data pipelines — schema checks, completeness, drift detection, and reconciliation across raw processed sanitized stages.

Surface and characterize quality issues in ways that engineering and product can act on.

You'll build the detection models, validation infrastructure, adversarial test sets, and audit processes that let us make strong claims about data quality and de-identification…

Strong Python for ML development and data validation (pytest, Great Expectations, Pandera, or similar).

Solid SQL and experience validating data across pipeline stages.

Familiarity with sensitive data categories and the relevant standards — HIPAA Safe Harbor for PHI, GDPR/LGPD for PII, PCI DSS for cardholder data…

Experience building and testing NER or other ML-based detection systems: building labeled eval sets, computing precision/recall, handling non-determinism.