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.
Develop and optimize radar-based perception pipelines - preprocessing, detection, clustering, and tracking
Train, deploy, and monitor radar object detection models for ground handling environments
Fuse radar data with camera and/or LiDAR for robust multi-modal perception
Build and maintain radar data collection, labeling, and evaluation pipelines
Architect a deterministic secondary perception system with radar as a primary modality
Mentor junior engineers on radar perception best practices
Bachelor's or Master's degree in Computer Science, Robotics, Electrical Engineering, or a related field
3+ years of focused experience in radar perception (signal processing, clustering, detection, tracking)
Deep understanding of radar fundamentals - range-Doppler processing, CFAR detection, beamforming, ghost/multipath mitigation
Experience with automotive or industrial radar sensors (continental, TI, Oculii, or similar)
Python, C++, and ROS2 proficiency
PhD in Computer Science, Robotics, Electrical Engineering, or a related discipline
Experience deploying radar models to production
Experience developing CUDA kernels
Experience fusing radar with camera/LiDAR in a production stack
Familiarity with radar-specific datasets (RADIal, RadarScenes, CARRADA, or similar)
Job details are sourced from the employer's original posting.
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AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. They are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving.