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Machine Learning / Data Engineer

Training Superintelligence· Frontier Data Operations
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Remotefull-timePosted 2w ago

About the role

Full-time remote role focusing on ensuring data quality and de-identification of sensitive data (PII/PHI/CII/financial) across enterprise data pipelines, through ML-based detection, validation infrastructure, and audit processes.

Responsibilities

  • 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
  • Design, train, and evaluate ML models (NER and other approaches) that detect sensitive entities across text and image-based documents
  • Build replacement pipelines that substitute detected entities with coherent alternatives, so the same entity always maps to the same replacement across every file in a corpus
  • Run these algorithms over large volumes of data to prepare it for downstream agentic task building
  • Build adversarial test sets for de-identification across all sensitive data classes: edge cases, obfuscated identifiers, multilingual entities, OCR noise, and formats designed to slip past detectors
  • Cover company identifiable information specifically — organization names and aliases, domains and email patterns, internal project and system names, org charts, vendor and partner relationships, contract terms, and any combination of details that could re-identify the source enterprise
  • Cover financial data — account and routing numbers, card numbers, revenue and pricing figures, transaction records, tax IDs, and financial statements
  • Measure and report de-identification performance by data class — entity-level precision and recall, leak rates, false-negative audits, and replacement consistency
  • Implement regression gates in CI/CD so no pipeline change ships without passing data quality and sensitive-data checks
  • Run sampling-based human-in-the-loop audits and maintain the audit trail as compliance evidence
  • Partner with engineering on root-cause analysis when inconsistencies or leaks are found, and drive fixes to closure

Qualifications

  • About 4 to 5 years of hands-on machine learning experience, with ML as your 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 the relevant standards — HIPAA Safe Harbor for PHI, GDPR/LGPD for PII, PCI DSS for cardholder data, and 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 a fast-moving environment
  • A skeptical, detail-oriented approach — you assume things are broken until you've proven otherwise

Skills

Required:Machine learning for NER / sensitive data detectionPython for ML developmentData validation and testingSQL and data pipeline validation

Benefits

  • Values-driven culture with client-first mindset
  • Opportunity to work at AI frontier and contribute to leading AI research
  • Collaboration with experienced colleagues from top tech companies
  • Startup-speed environment with ownership and impact
  • Equal opportunity employer with emphasis on diversity and inclusion
Last updated Oct 21, 2026. Interested? Apply on the company site.

About Training Superintelligence

Turing develops large-scale RL environments and data generation systems that train multimodal agents to improve model performance in coding, real-world, economically valuable tasks, and advanced STEM reasoning.

Train AIBuild AIHire AI TalentAI for BFSIEMNAIDatasetsEnterprise Knowledge WorkMultimodal
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