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Strong employer brand plus a visible ML title yields moderate applicant competition.
Automotive driver-modeling specialization and safety-critical requirements make background fit highly domain-sensitive.
Requires advanced degree, specialized driver-modeling expertise, and embedded deployment experience, so screening is strict.
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Lead R&D to develop novel algorithms for driver modeling including multi-modal sensor data interpretation and behavior analysis.
Design, train, and optimize deep learning and traditional CV models for real-time, embedded automotive systems targeting driver state and intent recognition.
Manage large-scale automotive datasets and contribute to IP via publications or patents in AI/ML for driver monitoring.
Master's or PhD in AI/ML, Computer Science, Electrical Engineering, or related field, or equivalent practical experience.
Strong programming skills in Python and C++ with experience optimizing code for performance.
Proven hands-on experience building and deploying perception systems for driver modeling or automotive interior sensing.
Deep knowledge of computer vision, machine learning frameworks (PyTorch, TensorFlow), and core mathematical/statistical concepts.
Expertise in driver modeling and human-centric AI applied to automotive safety and interior monitoring.
Experience publishing in top-tier AI/Computer Vision conferences related to behavioral cue analysis or driver monitoring.
Skillset includes multi-modal sensor fusion, embedded system optimization, and real-time system deployment in automotive contexts.