Senior Technical Program Manager, Data Labeling, Data and Machine Learning

Job Description: • Lead cross-functional initiatives to build, scale, and optimize data annotation programs critical to AI model performance. • Own program delivery across internal teams, vendor partners, and ML stakeholders to ensure high-quality labeled datasets are delivered on time and at scale. • Define and drive end-to-end execution of large-scale annotation programs across multiple data types. • Collaborate with ML, product, and data operations teams to scope and prioritize labeling needs. • Own vendor engagement: onboarding, SLA management, training, and quality reviews. • Build feedback loops between annotators and model performance to inform labeling strategies. • Create dashboards and reporting mechanisms to track labeling velocity, quality, and arenaflex. • Lead initiatives to improve labeling efficiency through tooling enhancements and process automation. • Be the voice of labeling in cross-functional forums-translating model needs into operational plans. • Manage and mentor a team of trained threat analysts who conduct our labeling. • Conduct analysis of the quality of the labeling and for insights into how our detections can be improved. • Hire and train new or replacement threat analysts Requirements: • 5+ years of program management experience, ideally in ML ops, data labeling, or AI infrastructure. • Proven track record building and managing remote labeling teams. • Strong understanding of ML lifecycle stages and the importance of annotated data quality. • Experience defining SOPs, audit mechanisms, and workflows for scalable data labeling. • Proficient in project management tools such as Jira, Asana, or Linear for program tracking • A deep understanding on ML Operations labelling tools and experience building or maintaining an annotation tool. • Strong analytical and communication skills; ability to synthesise feedback from ML, ops, and product stakeholders and also analyzed data to arenaflex trends in our labeling or detection quality. • Understanding of data privacy and security standards and how they can be followed in a labeling program. Benefits: • Health insurance • Flexible work arrangements • Professional development opportunities Apply tot his job Apply tot his job