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Remote mid-level program role with popular title and modest brand makes it moderately competitive.
Skills are transferable across ML labeling and data operations, but vendor-specific experience increases domain bias.
Explicit 3–6 years plus mandatory Python, vendor and SLA experience increases filter strictness.
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Manage and scale production annotation operations supporting computer vision and machine learning initiatives.
Own vendor relationship and ensure timely delivery of high-quality labeled datasets through workload planning, throughput monitoring, and SLA management.
Develop and maintain annotation SOPs, perform quality assurance through inter-annotator agreement analysis, and build Python-based workflows for annotation data handling and reporting.
3–6 years of experience in data operations, annotation program management, ML data engineering, or related roles.
Experience managing external annotation vendors under production SLAs.
Strong Python programming skills for data preparation, automation, and API integrations.
Experience writing annotation guidelines or SOPs and familiarity with cloud storage platforms (Azure Blob Storage or AWS S3).
Experienced in coordinating multiple concurrent annotation programs using Jira and managing external vendor relationships with operational accountability.
Technically adept with Python scripting to automate workflows and improve annotation data quality and throughput.
Able to analyze annotation quality issues, lead calibration sessions, and implement continuous improvements in labeling accuracy.