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Turing accelerates superintelligence by generating high-quality datasets, reinforcement learning environments, and frontier research benchmarks for AI labs, and by deploying agentic AI systems for Fortune 500 enterprises. This is a hands-on individual contributor role focused on data quality and sensitive data compliance.
You will own two critical functions: ensuring enterprise data flowing through Turing's connectors is what we think it is, and preventing any sensitive information—PII, PHI, company identifiable information (CII), or financial data—from passing downstream. You'll work closely with a senior ML lead with no client-facing responsibilities.
Data Quality responsibilities include running deep dives into enterprise data to assess quality (topic coherence, domain depth, completeness, consistency), designing and automating validation suites for data pipelines (schema checks, completeness, drift detection, reconciliation across raw → processed → sanitized stages), and surfacing quality issues in actionable ways for engineering and product teams.
Sensitive Data Compliance responsibilities include designing, training, and evaluating ML models (NER and other approaches) that detect sensitive entities across text and image-based documents (scans, invoices, presentations); building replacement pipelines that substitute detected entities with coherent alternatives so the same entity always maps to the same replacement; running these algorithms over large data volumes; building adversarial test sets for de-identification covering edge cases, obfuscated identifiers, multilingual entities, OCR noise, and formats designed to evade detectors; covering company identifiable information (organization names, aliases, domains, email patterns, internal project names, org charts, vendor relationships, contract terms); covering financial data (account/routing numbers, card numbers, revenue figures, transaction records, tax IDs, financial statements); measuring and reporting de-identification performance by data class (entity-level precision/recall, leak rates, false-negative audits, replacement consistency); implementing regression gates in CI/CD; running sampling-based human-in-the-loop audits and maintaining audit trails as compliance evidence; and partnering with engineering on root-cause analysis and fixes.
Why this matters: Enterprise customers trust Turing with their data on the condition it can never be traced back to them. Every downstream model, dashboard, and customer commitment depends on clean data and an airtight sanitization layer. Finding a leak prevents an incident; proving there isn't one earns the trust that lets the company move fast.
Requirements:
- 4–5 years of hands-on machine learning experience with ML as primary background
- 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 relevant standards: HIPAA Safe Harbor for PHI, GDPR/LGPD for PII, PCI DSS for cardholder data, confidentiality/NDA obligations for company information
- Experience building and testing NER or other ML-based detection systems: building labeled eval sets, computing precision/recall, handling non-determinism
- Understanding of re-identification risk—how seemingly innocuous details combine to reveal an organization or individual
- Comfort with ambiguity and fast-moving environments
- Skeptical, detail-oriented approach; assume things are broken until proven otherwise
Nice to have:
- Computer vision and OCR experience, especially building, scaling, and evaluating document pipelines for contracts, statements, invoices, presentations, internal documents
- Hands-on experience with financial or healthcare data, including privacy requirements specific to those industries
- Startup experience
- Auditing LLM or VLM outputs
- Synthetic sensitive-data generation (PII, PHI, company and financial records)
- Familiarity with GCP data stack (BigQuery, GCS, Cloud Run jobs) and CI/CD integration
- Experience handling multi-tenant enterprise data with strict customer confidentiality requirements
- Compliance reporting or working with auditors
