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 To this Job Company : careerbox.42web Salary : Work from home 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 To this Job Company : careerbox.42web Salary : Work from home