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Metro Bengaluru senior ML role with ADAS specialization reduces but still attracts capable ML applicants.
Requires automotive ADAS, sensor and ECU knowledge, and embedded signal familiarity limiting cross-industry transferability.
Explicit 6-8 years, required ML/DL, MLOps, distributed training, and automotive validation skills drive strict filtering.
Design, develop, train, and validate ML and Deep Learning models for ADAS/Automotive validation use cases using large-scale vehicle datasets.
Build and maintain end-to-end ML pipelines covering data acquisition, preprocessing, labeling, feature engineering, model training (including distributed training on AWS/Azure/HPC), and performance evaluation.
Collaborate with algorithm and validation teams to improve model robustness, conduct root-cause analysis on failures, and support deployment readiness and validation workflows.
Bachelor's or Master's degree in CS, AI, Machine Learning, Data Science, Electrical or Computer Engineering, or related field.
6-8 years of industry experience in Machine Learning, Data Science, Deep Learning, or related fields.
Strong Python development skills with hands-on experience building production-grade ML pipelines and training models from scratch with large-scale datasets.
Experience with automotive domain signals, ECUs, CAN, Ethernet, and embedded systems fundamentals.
Experienced in end-to-end ML pipeline development focusing on perception, signal processing, and validation in automotive environments.
Familiar with distributed training, model evaluation, validation frameworks, and managing large-scale automotive datasets.
Skilled in collaborating across algorithm, systems, and validation teams to enhance model robustness and solve complex ML problems in ADAS contexts.