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Tier-1 brand and mid-level experience increase competition, CV/ADAS specialization moderates applicant density.
Computer vision skills transfer across robotics and autonomy, but ADAS domain experience increases specificity.
Explicit 3–8 year requirement, mandatory CV/ML skills and DL framework experience enforce strict filters.
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Own the development, training, and validation of deep learning models for perception tasks like object detection, semantic segmentation, and lane detection within L2+ ADAS systems.
Analyze large-scale video datasets to extract insights and curate datasets for model training, and perform rigorous experimental design and analysis to improve model performance.
Contribute to building and maintaining MLOps pipelines for data processing, model training, and deployment; implement and optimize algorithms in Python for real-time embedded systems.
Bachelor’s degree in Computer Science, Electrical Engineering, or a related field.
3-8 years of professional experience in computer vision or machine learning application development.
Proficiency in Python and strong understanding of object-oriented programming.
Strong hands-on experience with modern deep learning frameworks such as PyTorch or TensorFlow 2+, and familiarity with development tools like Git, Docker, and Linux.
Experienced engineer focused on computer vision applications, particularly in deep learning model development for perception systems.
Ability to analyze complex datasets and design experiments to identify model failure modes and improvements.
Experienced in operationalizing ML workflows with MLOps practices, including data processing, training, and real-time deployment optimizations.