This is a hands-on role at the intersection of research, data science, and operations ideal for someone who thrives in fast-paced, cross-functional environments.
Key Responsibilities:
Own and optimize the end-to-end data flow across internal systems and platforms.
Build, manage, and mentor a team working across diverse data types: images, text, floorplans, and more.
Define, refine, and enforce annotation guidelines and quality standards.
Monitor data quality metrics, accuracy, consistency, and edge case handling.
Act as a player-coach: participate directly in annotation tasks while overseeing daily operations.
Identify and recommend tools and technologies to optimize workflows.
Collaborate with ML engineers and data scientists to optimize processes.
Identify and implement tools, technologies, and process improvements in close collaboration with ML engineers and data scientists to optimize end-to-end workflows.
2+ years of experience managing annotation or data labeling operations, ideally in a startup environment.
Excellent attention to detail, problem-solving skills, and ability to handle edge cases.
Strong knowledge of data labeling and data operation processes for AI/ML, including edge cases and ambiguity resolution.
Familiarity with annotation tools (e.g., Labelbox, Scale AI, CVAT, Prodigy, or custom tools).
Basic understanding of machine learning workflows and the impact of high-quality labeled data.
Strong communication and leadership skills to manage a diverse annotation team.
Ability to analyze and refine workflows for efficiency and scalability.
Comfortable working in a fast-paced startup environment with evolving priorities.
