Machine Learning, Data Engineer

This is a fully remote position, open to applicants in Brazil, +1 more country.

📋 Description

• Evaluate the quality of enterprise data, focusing on topic coherence, domain depth, completeness, and consistency.

• Develop and automate validation frameworks for data pipelines, which include schema validation, completeness checks, drift detection, and reconciliation across raw, processed, and sanitized data stages.

• Identify quality issues for engineering and product teams to address.

• Design, train, and assess machine learning models such as Named Entity Recognition (NER) to identify sensitive entities in both text and image documents.

• Construct replacement pipelines that reliably replace detected entities throughout corpus files.

• Execute detection and replacement algorithms on large datasets.

• Create adversarial de-identification test sets that encompass edge cases, obfuscated identifiers, multilingual entities, OCR noise, and evasive formats.

• Identify company-specific information and financial data.

• Evaluate and report on de-identification effectiveness using metrics such as precision, recall, leak rates, false-negative audits, and replacement consistency.

• Establish CI/CD regression gates for data quality and checks on sensitive data.

• Perform human-in-the-loop audits and maintain compliance audit trails.

• Collaborate with engineering teams on root-cause analysis and ensure resolutions are implemented effectively.

⛳️ Requirements

• Approximately 4 to 5 years of practical experience in machine learning, with a strong focus on ML as the core expertise.

• Proficient in Python for machine learning development and data validation (using tools such as pytest, Great Expectations, Pandera, or similar).

• Strong SQL skills and experience in validating data throughout pipeline stages.

• Knowledge of HIPAA Safe Harbor for PHI, GDPR/LGPD for PII, PCI DSS for cardholder data, and confidentiality/NDA requirements for company information.

• Experience in developing and evaluating NER or other ML-driven detection systems.

• Background in creating labeled evaluation datasets, calculating precision and recall, and managing non-determinism.

• Awareness of re-identification risks.

• Ability to navigate ambiguity and thrive in a rapidly changing environment.

• Detail-oriented and skeptical mindset.

• Experience in computer vision and OCR, familiarity with financial or healthcare data, prior experience in startups, auditing LLM/VLM outputs, generating synthetic sensitive data, utilizing GCP data stack, handling multi-tenant enterprise data, and compliance reporting are advantageous.

🏝️ Benefits

• Full-time remote work flexibility.

• Chance to work at the cutting edge of AI alongside leading AI research labs.

• Opportunities to engage in forefront AI research and present findings at ICLR, ICML, and NeurIPS.

• Opportunity to leverage advanced AI innovations to tackle enterprise business challenges.

• Collaborative environment with opportunities to learn from colleagues with extensive AI expertise.

• Fast-paced startup environment with opportunities for ownership and impact.

• Commitment to equal opportunity, diversity, inclusivity, and authenticity in the workplace.