AI/ML Engineer

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AI / ML Engineer Dubai About the Company: Our client is an AI and robotics startup building autonomous systems for heavy duty environments. Based in the UAE, they design, build and deploy intelligent autonomy end-to-end, helping enterprise clients bring automation into complex, high risk operations. The Role You will own how the vehicles see and understand the world. That means building the perception layer that turns raw sensor data into a reliable picture of the environment in real conditions: dust, glare, heat, uneven terrain and places no one has mapped. Your work feeds directly into navigation, obstacle avoidance and fleet management. This is a hands-on role with high ownership, and you will be expected to get code running on real vehicles early. What You Will Do Design, build and deploy the perception stack across camera, LiDAR and radar inputs Develop object detection, tracking and segmentation that holds up in harsh outdoor and industrial settings Build localisation and mapping capability, including operation in GPS-denied and unmapped areas Fuse multi-sensor data into a unified, real-time environmental model the planning stack can trust Optimise models for on-vehicle inference on edge hardware within strict latency and compute budgets Build data pipelines for collection, labelling, validation and retraining from fleet data Feed perception insights into fleet-level analytics for mission planning and performance monitoring Work closely with autonomy, controls and hardware engineers to take features from test track to deployment What You Bring 3+ years building perception systems that have run on real robots or vehicles, not only curated datasets Strong computer vision skills: detection, tracking and segmentation on live sensor feeds Hands-on experience with SLAM, point cloud processing and multi-sensor calibration Experience with sensor fusion (e.g. Kalman filtering, factor graphs or learned fusion approaches) Strong Python and C++, Experience deploying models to edge or embedded GPU hardware Comfort with ROS/ROS 2 and field testing Ownership mindset Model compression and optimisation (quantisation, pruning, TensorRT or similar) Experience with fleet data infrastructure and MLOps for continuous model improvement Exposure to simulation environments for perception testing Why Apply Build technology that runs on real vehicles in real missions Early-stage ownership in a fast-growing startup