ML Engineer

ML Engineer Role Requirements

• Experience: 3-6+ years in ML engineering, data science, model evaluation, ML QA, applied NLP evaluation, or data-heavy quality engineering.
• Tech Stack: Strong proficiency in Python and advanced data analysis skills required.
• Evaluation Expertise: Deep understanding of precision/recall/F1 calibration, confidence thresholds, dataset splits, leakage detection, overfitting prevention, statistical testing, and comprehensive error analysis.
• Pipeline Building: Proven experience constructing evaluation pipelines, benchmark suites, test harnesses, dashboards, or regression frameworks.
• Data Quality Management: Ability to effectively work with imperfect labels, annotation disagreement, clinical ambiguity, and hidden evaluation sets.
• Mindset: Strong independence and judgment; capability to challenge releases when evidence is insufficient.
• Communication: Clear written communication skills for producing release reports, incident analysis, and making critical quality decisions.