Professional Data Annotation Analyst – Rwanda (Full-time)

About Zipline Zipline is the world’s largest and most experienced drone delivery service. We are on a mission to serve all humans equally by ensuring access to food, medicine and essential goods anytime, anywhere. We design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably. Today, Zipline operates on four continents, makes a delivery somewhere in the world every 30 seconds, and has completed millions of deliveries to date, including blood, vaccines, medical supplies, food, and retail products. Our customers include the world’s largest and most prominent healthcare systems, governments, retailers, restaurants and global businesses who rely on us to save lives, reduce emissions, increase economic opportunity, and provide delivery from point A to point B as fast as possible. The drone is only 15% of what we’ve built to enable seamless, reliable, global operations. Our system strengthens supply chains, reduces congestion, and gives people time back. With more than 140 million commercial autonomous miles safely flown, Zipline is redefining access to healthcare, consumer products, and food across the globe. We operate at a global scale and are looking for practical problem solvers who thrive on real-world challenges and rapid growth. Our team is motivated by building systems that have a direct, meaningful impact on people’s lives and by scaling the future of logistics. We are seeking people who sculpt from first principles, enjoy facing adversity, and can do the impossible at record breaking speeds. About You And The Role Zipline’s autonomous delivery systems depend on perception data that helps our aircraft understand the world around them, including detecting other aircraft, identifying ground-based obstacles, and supporting accurate deliveries. As a Data Labeling Specialist, you’ll play a key role in supporting Zipline’s global operations through careful sensor-data labeling and review work that turns raw operational data into reliable training and evaluation datasets for machine learning. You will apply annotation guidance consistently, identify edge cases, data-quality issues, and tool challenges, and share clear feedback with Autonomy partners so perception development stays grounded in accurate, high-quality data. What You’ll Do Label and review data from multiple sensor streams using internal and external tools, adding the metadata needed for machine-learning applications. Build expertise across different sensor-data workflows and apply annotation guidelines consistently to produce accurate, complete, and high-quality labeled datasets. Identify data anomalies, edge cases, recurring ambiguities, and opportunities to improve labeling and review processes. Document and escalate quality, tool, or system issues that may affect labeling accuracy, throughput, or downstream perception development. Deliver high quality labeled datasets on a regular cadence to support critical algorithm-development timelines. Provide structured feedback to Autonomy teams on labeling tools and workflows, helping improve usability, efficiency, and review quality. Support Autonomy teams on related projects as needed. What You’ll Bring Bachelor’s degree in an Engineering field, or completion of final-year academic requirements with graduation pending. Experience reviewing and labeling data, with the discipline

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