• Create high-quality instructional materials that teach modern data annotation and labeling workflows used to train and evaluate machine learning systems. • Develop content across modalities—including text, image, video, audio/speech, and cross-modality labeling. • Collaborate with internal program team to produce session plans, hands-on practice, examples, and assessments aligned to defined learning outcomes. • Content should model best-in-class annotation behavior: rule-based decisions, clear rationales, consistent application of guidelines, and appropriate handling of ambiguity. • Produce complete content packages per session/module, such as facilitator guide, learner materials, demos or walkthroughs, practice activities, strong vs. weak examples, rubrics/evaluation criteria, and short knowledge checks/assessments. • Create hands-on exercises using a common annotation platform, including task setup guidance, labeling instructions, example labels, edge cases, and review workflows. • Review and iterate on content based on internal feedback and peer review. • Participate in weekly and ad hoc meetings with the program team to align on objectives, standards, and delivery constraints. • Support content deployment into our learning platform (training provided).