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Remote role and mid-level (2–5 years) amplifiers increase candidate density.
Specialized ML annotation and computer vision experience needed, but skills largely transferable across AI/ML teams.
Explicit 2–5 years, mandatory ML annotation experience, plus required Python, SQL, and domain-specific QA skills.
Own the quality analysis of annotated data for computer vision and machine learning programs, including daily review and root cause determination.
Develop, version, and maintain verdict taxonomies, decision rules, and quality guidelines for annotation categories.
Build and manage performance trackers to monitor error rates by facility, site, equipment, and data format, perform anomaly detection, and report findings with reproducible evidence promptly.
BS in Computer Science/Engineering, Electrical Engineering, or equivalent experience.
2–5 years experience in ML QA, annotation quality, data quality, or analytics at an AI/ML company.
Proficient in Python and SQL (or equivalent) for querying and analyzing data independently.
Experience assessing label quality on annotated datasets for computer vision/machine learning.
Experienced in working directly with annotation vendors and responsible for in-house judgment-heavy quality control in ML annotation.
Strong analytical skills with proven ability in root cause analysis distinguishing annotation errors from model or field degradation.
Comfortable creating and evolving annotation quality processes, guidelines, and tooling to reduce manual work and improve annotation accuracy.