Key Skills

Job Description

Who We Are AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com. You will Architect 3D object detection models, including multi-modal approaches (camera, LiDAR, Radar). Build and maintain data collection and evaluation (metrics) pipeline. Develop a proprietary multi-modal dataset for training and evaluation. Train, deploy and monitor 2D/3D object detection models to production. Build and execute a roadmap for the perception system Mentor junior engineers about best practices. You have Master’s or PhD in Computer Science, Robotics, Deep Learning, or a related field. 7+ years of focused experience in architecting and deploying 2D/3D object detection models. Worked on entire perception stack (sensing, preprocessing, detection & tracking) Python proficiency The ability to identify gaps and lead cross functional projects We Prefer PhD in Computer Science, Robotics, or a related discipline Full stack AV knowledge Experience in architecting perception systems (detection & tracking) Publications in major conferences like CVPR, ICRA, NeurIPS, IJCAI, AAAI etc

Core Responsibilities

Architect, train, deploy, and monitor multimodal 2D and 3D object detection models, while building the data collection, evaluation, and proprietary dataset pipelines. Set the perception system roadmap, lead cross-functional initiatives, and mentor junior engineers.

Requirements

Requires a master’s degree or PhD in computer science, robotics, deep learning, or a related field, along with 7+ years of focused experience architecting and deploying 2D/3D object detection models. Candidates should have experience across the perception stack and proficiency in Python; a PhD, full-stack autonomous vehicle knowledge, perception-system architecture experience, and major conference publications are preferred.