The AI/ML Annotation Operations team is a fast paced, multifaceted environment. We are seeking a highly motivated, experienced manager to lead an operations team and deliver on several complex annotation projects across Apple. You will lead a team in the study of data requirements for machine learning engineering teams, drive the creation and optimization of annotation processes, and monitor the operations lifecycle to ensure that project goals and achievements are met.
If you are a smart, creative, engaging project and people manager, self-motivated teammate, who thrives in a fast-paced, constantly changing environment, passionate about building great products, doing the right thing and learning new technologies, this is the job for you.
This is a multi-functional role that requires working with the Customer, Engineering, Quality, Training and Production Operations teams to deliver premier solutions. Relationship building and strong technical skills are critical.
You are a proven project and people manager who can thrive in a fast-paced workplace where both individual drive and team collaboration are the keys to success. You will collaborate with the engineering teams and data requestors to deploy scalable data pipelines in production. You will plan and track delivery timelines, provide technical expertise and leadership throughout a data request life cycle to ensure the final dataset meets the customers quality expectations. You will deliver monthly volume forecast & capacity planning and handle resource management. You will oversee operational responsibilities including workflow management, process improvement and operations health review, as well as track quality assurance status and training needs. You will engage with Customers to clarify quality and data requirements, discuss edge cases and receive early quality feedback for new annotation types. You will identify potential areas of tools and process improvements with impact analysis, scope clear requirements and work with the prototyping team to test these approaches before deploying them in production.
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