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 Apply tot his job