AI/ML Expert

We are seeking a highly skilled and motivated AI/ML Researcher to design, develop, and deploy cutting-edge computer vision systems specifically for driver modeling applications. This role is pivotal in enhancing the safety, comfort, and personalization of our next-generation automotive interior monitoring products. The ideal candidate will combine strong research skills with practical engineering experience to translate innovation into production-ready, robust, and reliable driver modeling solutions for the automotive industry.

Responsibilities

Research & Development for Driver Modeling: Conduct pioneering research to develop novel algorithms and models specifically tailored for critical driver modeling tasks, including domain-aware representation learning, driver state and behavior analysis, driver intent and activity recognition, and 3D interior scene and driver interaction understanding; Algorithm Implementation & Optimization for Automotive: Design, train, and optimize deep learning models (e.g., CNNs, Transformers, Recurrent Neural Networks) and advanced traditional computer vision algorithms for core perception tasks central to driver modeling, utilizing multi-modal sensor data commonly found in automotive environments (e.g., RGB, IR, Depth cameras); Automotive-Grade Data & Pipelines: Devise comprehensive data collection, annotation, and augmentation strategies specifically for diverse driver populations and real-world driving scenarios; Manage and curate large-scale, high-quality automotive datasets for model training, validation, and benchmarking; Stay abreast of the latest advancements in AI/ML and computer vision research relevant to driver modeling, contributing to the company's IP through patents or publications

Requirements

Master's or PhD in AI/ML, Computer Science, Electrical Engineering, or a related technical field, or equivalent practical experience with a strong focus on computer vision for automotive applications; Strong programming skills in Python and C++, with experience in optimizing code for performance; Deep understanding of computer vision fundamentals, image processing, and proven experience with both traditional computer vision techniques (e.g., feature descriptors, geometric vision, optical flow) and machine learning/deep learning frameworks (such as PyTorch and TensorFlow); Ability to intelligently combine these approaches for robust solutions; Demonstrable hands-on experience building, training, and deploying perception systems specifically for driver modeling, human-centric AI, or related automotive interior sensing applications; Solid grasp of mathematical, statistical, and information theory concepts, including linear algebra, calculus, probability theory, stochastic processes, and concepts like entropy; Excellent analytical and problem-solving skills, with the ability to tackle complex, novel challenges inherent in developing safety-critical automotive systems; B-Tech / M.Tech

Nice to have

Publications at top-tier AI/CV conferences (e.g., CVPR, ICCV, ECCV, NeurIPS) related to human pose estimation, facial analysis, gaze tracking, activity recognition, feature engineering for behavioral cues, or driver behavior analysis; Experience with multi-modal sensor fusion techniques (e.g., camera, RGB/IR, radar) for robust perception in automotive environments; Experience with model compression, quantization, and deployment on embedded automotive hardware or resource-constrained devices; Experience designing and optimizing algorithms for real-time performance in production environments