Join our dynamic team in the automotive industry as an AI Research Engineer, focused on Computer Vision, and contribute to the development of cutting-edge AI solutions. In this role, you will focus on building and optimizing computer vision algorithms for tasks such as video understanding, scene analysis, and real-time processing. Your work will help enhance vehicle safety, improve user experiences, and enable innovative in-vehicle applications.

●      Develop and implement computer vision algorithms for tasks including object detection, activity recognition, and video analysis.

●      Train, fine-tune, and evaluate machine learning models using automotive and large-scale datasets.

●      Deploy and optimize models for edge devices and real-time automotive applications.

●      Collaborate with cross-functional teams to integrate computer vision solutions into in-vehicle systems and infotainment platforms.

●      Conduct research to stay up-to-date with advancements in video understanding and computer vision technologies.

●      Create scalable, maintainable pipelines for data processing, model training, and deployment.

●      Document workflows, algorithms, and results for internal and external stakeholders.

●      MS or PhD in Computer Science, Electrical Engineering, or a related field.

●      Strong programming skills in Python, with experience in deep learning frameworks like TensorFlow or PyTorch.

●      Proficiency in computer vision techniques, including CNNs, transformers, and video modeling.

●      Hands-on experience with video data and tasks such as action recognition or video classification.

●      Familiarity with image and video processing libraries (e.g., OpenCV, scikit-image).

●      Experience with hardware accelerators (e.g., GPUs, NPUs) for model training and deployment.

Preferred Qualifications
●      Authored or co-authored publications in top-tier computer vision, machine learning, or AI conferences/journals (e.g., CVPR, ICCV, NeurIPS, ICML).

●      Knowledge of spatiotemporal modeling and video understanding techniques.

●      Familiarity with MLOps practices, including model deployment pipelines and CI/CD.

●      Background in optimizing models for resource-constrained and edge environments.

●      Experience with annotation tools and synthetic data generation for training datasets.