1 month ago
São Paulo, BrazilSenior
Responsibilities
- Assess enterprise data quality across connectors, including topic coherence, domain depth, completeness, and consistency.
- Design and automate pipeline validation for schemas, completeness, drift, and reconciliation across raw, processed, and sanitized stages.
- Design, train, and evaluate ML models such as NER systems for detecting sensitive entities in text and image-based documents.
- Build scalable replacement pipelines that consistently substitute detected entities while preserving data usefulness.
- Create adversarial de-identification test sets covering edge cases, obfuscation, multilingual entities, OCR noise, and sensitive-data classes.
- Measure precision, recall, leak rates, false-negative audits, and replacement consistency by data class.
- Implement regression gates, human-in-the-loop audits, audit trails, and compliance evidence.
- Partner with engineering on root-cause analysis and drive fixes for data inconsistencies and sensitive-data leaks.
Requirements
- About four to five years of hands-on machine learning experience, with ML as the primary background.
- Strong Python skills for ML development and data validation, including pytest, Great Expectations, Pandera, or similar tools.
- Solid SQL skills and experience validating data across pipeline stages.
- Familiarity with PII, PHI, CII, and financial-data categories and standards including HIPAA Safe Harbor, GDPR/LGPD, PCI DSS, and confidentiality or NDA obligations.
- Experience building and testing NER or other ML-based detection systems, labeled evaluation sets, precision/recall metrics, and non-deterministic systems.
- Understanding of re-identification risk and how combined details can reveal an organization or individual.
- Computer vision and OCR experience, financial or healthcare data experience, LLM or VLM output auditing, synthetic sensitive-data generation, GCP data-stack experience, multi-tenant enterprise data, compliance reporting, or auditor collaboration are preferred.
- Comfort with ambiguity, fast-moving environments, and detailed skeptical investigation.
Benefits
- Full-time remote role based in Brazil or Colombia.
- Opportunity to work at the frontier of AI and contribute to datasets, reinforcement-learning environments, benchmarks, and enterprise agentic AI systems.
- Opportunity to contribute to leading-edge AI research and showcase work at conferences such as ICLR, ICML, and NeurIPS.
- Collaboration with experienced colleagues from companies including Google, Meta, and Amazon.
- Startup-speed environment with ownership and impact.
- Equal opportunity employer committed to a diverse and inclusive workplace.
Tech Stack
Categories
Data EngineeringML Engineering
About Turing
Turing builds AI data pipelines and systems for enterprises and AI labs. It creates large-scale datasets and reinforcement-learning environments to train and evaluate models, and deploys agentic AI applications in domains like software engineering and enterprise workflows. The company also operates an AI-powered talent cloud to match and manage developers and AI trainers. Founded in 2018 and headquartered in San Francisco, it is privately held and serves Fortune 500 customers.