Senior Machine Learning Engineer

Your RoleSr. Machine Learning EngineerROLES 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.