ML and Data Engineer, Data Quality and Privacy

You will improve the reliability of enterprise data pipelines and evaluate systems that remove personally identifiable information and protected health information. The work combines automated testing, adversarial test data, data quality analysis, machine learning evaluation, and privacy-focused review.

• Create validation suites for schema integrity, completeness, drift detection, and reconciliation across pipeline stages.
• Review enterprise data sources and connectors for coherence, domain coverage, consistency, and depth.
• Build adversarial test datasets with obfuscated identifiers, multilingual entities, OCR noise, edge cases, and unusual document formats.
• Evaluate named entity recognition and other machine learning de-identification systems with precision, recall, F1 score, leak rates, and false-negative analysis.
• Investigate privacy leakage across raw, processed, and sanitized data.
• Add regression gates, monitoring, reporting, sampling audits, and compliance trails to data and engineering workflows.

What It Pays and Takes

This is a remote contractor role marked as entry level. Compensation is not disclosed.

• Availability: 20 or more hours per week.
• Schedule: Includes six hours of daily overlap with Pacific Time.
• Contract: Initial one-month engagement, with the term subject to adjustment.
• Location: Open worldwide.
• Language: Fluent in English.
• Experience: Five or more years in data engineering, machine learning engineering, data quality, or a related field.
• Technical skills: Strong Python and SQL skills, including test automation and validation across pipeline stages.
• Privacy knowledge: Understanding of PII, PHI, de-identification, and frameworks such as HIPAA Safe Harbor, GDPR, or LGPD.
• Evaluation experience: Experience using labeled datasets and precision and recall metrics to assess named entity recognition or other machine learning systems.
• Testing skills: Ability to design reliable evaluations for non-deterministic machine learning or large language model systems.
• Engineering workflow: Experience integrating automated tests and quality checks into CI/CD pipelines.
• Useful tools: pytest, Great Expectations, Pandera, BigQuery, Google Cloud Storage, or Cloud Run Jobs.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training is the human work behind systems that learn from examples, including data review, machine learning evaluation, and privacy testing. People with strong technical or subject-matter skills help make these systems more accurate, reliable, and safe.