Rwanda-Based Data Annotation Professional (Full-Time)

Lipline stands as the world’s foremost autonomous drone delivery service, distinguished by its unparalleled experience and global reach. Committed to equitable access, the company delivers essential goods—such as food, medicine, and critical supplies—rapidly and reliably to any location. Operating across four continents, Lipline achieves a delivery every 30 seconds, having executed millions of successful shipments that include blood, vaccines, medical equipment, food, and retail items. Trusted by leading healthcare systems, governments, retailers, and multinational corporations, Lipline enhances supply chains, reduces emissions, and accelerates delivery speeds from point A to point B. Though drones are central to its operations, they represent just 15% of the comprehensive system designed to ensure seamless, dependable global logistics. With over 140 million autonomous miles flown safely, Lipline is transforming healthcare access, consumer goods distribution, and food security worldwide. The company operates at a global scale and seeks practical problem solvers who excel in dynamic, high-growth environments and relish real-world challenges. Lipline’s culture thrives on innovation, impact, and urgency, attracting individuals who solve problems from first principles, embrace adversity, and achieve the extraordinary at unprecedented speeds. About the Role: The success of Lipline’s autonomous delivery systems hinges on precise perception data, enabling aircraft to navigate environments, detect airborne and ground-based hazards, and execute accurate deliveries. As a Data Labeling Specialist, you will be instrumental in refining global operations by meticulously labeling and reviewing sensor data, converting raw information into reliable datasets for machine learning. Your responsibilities include applying annotation guidelines with consistency, identifying edge cases and data-quality concerns, and providing clear feedback to Autonomy partners to ensure perception systems remain grounded in accuracy and integrity. Key Responsibilities: Label and review data from diverse sensor streams using both internal and external tools, incorporating necessary metadata for machine-learning applications. Develop deep expertise across sensor-data workflows while adhering to annotation guidelines to produce datasets that are precise, comprehensive, and high-quality. Detect data anomalies, edge cases, recurring ambiguities, and process inefficiencies, proposing improvements to enhance labeling and review workflows. Document and escalate quality, tool, or system issues that may compromise labeling accuracy, efficiency, or the downstream development of perception algorithms. Deliver meticulously labeled datasets on a consistent schedule to align with critical algorithm-development timelines. Offer structured feedback to Autonomy teams on labeling

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