Senior Machine Learning Engineer

Your Role  Sr. Machine Learning Engineer ROLES AND RESPONSIBILITIES Design, develop, train, fine-tune, and validate Machine Learning and Deep Learning models for ADAS/Automotive validation use cases. Build end-to-end ML pipelines including data acquisition, dataset curation, preprocessing, labeling, cleaning, feature engineering, and model evaluation. Develop and train Deep Neural Networks (DNNs), CNNs, RNNs, Transformers, and other state-of-the-art architectures for perception, signal processing, event detection, and validation workflows. Support initiatives such as TSI validation by training models from scratch using large-scale vehicle datasets. Define training, validation, and test datasets and establish data quality standards for model development. Analyze dataset quality, class imbalance, labeling accuracy, feature distributions, and data drift issues. Implement model training workflows on AWS, Azure, and HPC environments utilizing distributed training techniques when required. Perform hyperparameter tuning, model optimization, performance benchmarking, and error analysis. Develop evaluation frameworks and validation methodologies for ML model performance across different operating conditions. Collaborate with algorithm and validation teams to investigate model failures and improve model robustness. Create reproducible ML pipelines and maintain model training, experiment tracking, and documentation standards. Support deployment readiness reviews and provide technical guidance on model performance and limitations. Apply structured problem-solving techniques to support root-cause analysis. Learn ADAS system architecture and data flow from sensor → ECU → analytics → validation. Participate in hands-on vehicle testing and data collection under guidance of senior engineers. Support vehicle instrumentation, logging setup, and data validation. Help ensure collected data meets analysis and validation requirements. Extract and prepare large-scale automotive datasets required for machine learning model development and validation. Develop automated data processing pipelines for sensor, CAN, Ethernet, camera, radar, and vehicle telemetry data. Identify high-value training scenarios and edge cases for model development and validation. Collaboration & Process Adherence Work closely with Algorithm development team, Systems and validation engineers and Project and engineering managers Follow established ADAS V-cycle development, validation, and documentation processes. Contribute to continuous improvement of data analysis tools and workflows. Your Background Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, Computer Engineering, or related discipline. 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.