AI Engineer – Differentiable Systems for Automotive Perception

About the role

We are looking for an AI Engineer to design end‑to‑end differentiable systems that integrate sensor control, illumination patterns, and perception tasks such as detection and segmentation for automotive applications.

Key responsibilities

• Design and implement differentiable pipelines that jointly optimise sensor control, illumination and perception (detection, segmentation).
• Develop domain‑adaptation methods that enable synthetic‑to‑real transfer without retraining.
• Create energy‑aware AI models that balance perception quality with power constraints for embedded automotive deployment.
• Validate solutions through real‑world vehicle testing across diverse environmental conditions.
• Publish research at top‑tier conferences while delivering production‑ready solutions.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Data Science, Electrical Engineering or a related field.
• 3+ years of experience developing and deploying deep learning and reinforcement learning models.
• Strong background in object detection, semantic segmentation and neural rendering.
• Hands‑on experience with automotive sensors (cameras, LiDAR, radar, thermal imaging) and closed‑loop control integration.

Required skills

• Python, C++, CUDA programming.
• Deep learning frameworks: PyTorch, TensorFlow.
• Robotics middleware: ROS.
• Simulation environments: CARLA, Applied Intuition, NVIDIA platforms.
• 3‑D rendering engines.
• Domain adaptation, self‑supervised learning, model optimisation for edge devices.
• Experience with automotive perception datasets such as nuScenes, Waymo Open, KITTI.