ML Annotation QA Engineer

Overview

ML Annotation QA Engineer responsible for quality analysis of annotated data used to train and measure computer vision and machine learning systems.

What you'll do

• Own judgment-heavy quality analysis on annotated data that cannot be reliably outsourced.
• Manage the daily review queue and produce verdicts and root-cause findings in house.
• Own, version, and refine the verdict taxonomy, decision rules, and quality guidelines.
• Build and maintain performance trackers (error rates by facility, site, equipment, and data format).
• Detect anomalies against an agreed baseline and investigate root causes with stated confidence.
• Query and analyze annotation data using Python and SQL to test hypotheses independently.
• Report findings to engineering and ML with reproducible evidence and confidence, and feed SOP changes.

What you'll need

• 2–5 years of experience in ML QA, annotation quality, data quality, or analytics at an AI/ML company.
• Hands-on experience with annotated ML datasets, including assessing label quality.
• Strong understanding of statistics to work with data.
• Root cause analysis skills, generating competing hypotheses and naming separating evidence.
• Familiarity with Python and SQL (or equivalent) for independent querying and analysis.
• Ability to write quality guidelines, decision rules, and labeling taxonomies.
• Excellent documentation, communication, and collaboration skills.

Details

• Location: Open to Remote (India).
• First assignment includes a warehouse forklift vision program for barcode readability and localisation analysis.
• Works with an external annotation partner at production volume.