Data Ops Leader, Abridge

• 5+ years in data annotation, data operations, or related roles, with at least 2 years in a leadership position
• Experience working with annotation platforms (e.g., Labelbox, Scale AI, Supervisely)
• Familiar with the operations of machine learning research teams and the role of annotated data in model training
• Experience building and managing internal annotation teams
• Strong organizational and project management skills
• Experience working with cross-functional teams in a fast-paced, startup environment

Desirable

• As the Data Annotation Leader, you’ll play a pivotal role in enabling our AI applied science and product teams
• This role is responsible for building the datasets needed to train and evaluate the next generation of models powering Abridge’s products
• This role is a unique opportunity to stand up a new function on a team that is reshaping the practice of medicine
• You will work cross-functionally, collaborating closely with AI/ML scientists, product leaders, data engineers, and our legal team, developing processes to scale up data annotation efforts at Abridge
• Sourcing job specifications from stakeholders in scientists and product
• Working with our commercial, legal, and security teams to identify and uphold all relevant data security and privacy requirements
• Driving strategic decisions about which vendors to work with and when to spin up internal annotation processes
• Quarterbacking engagements with vendors, working with finance and company leaders to negotiate strong deals
• Overseeing the work to ensure the quality of work product and compliance with our requirements
• Stay on top of the landscape of vendors, including both capabilities and economics
• Drive strategic decisions about which annotation jobs to pursue with which vendor
• Quarterback discussions and negotiations with vendors in partnership with finance and leadership
• Ensure that our annotation investments align with Abridge’s research and product priorities, communicating frequently with builder leadership
• Work closely with scientists to produce clear specifications and identify appropriate datasets for each task
• Be the point person in charge of each annotation effort, ensuring that all efforts meet our product needs, comply with our obligations, and are completed swiftly
• Define and monitor quality metrics for annotation tasks
• Work with our AI science and engineering teams to curate the resulting datasets for model training and evaluation (storage, versioning, access controls, etc.)