Senior Machine Learning Engineer

Job description

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. • Demonstrated experience training Machine Learning and Deep Learning models from scratch using large-scale datasets. • Hands-on experience with dataset creation, data collection, labeling, cleaning, augmentation, feature engineering, and data quality assessment. • Strong understanding of statistical learning, model evaluation metrics, validation methodologies, and error analysis. • Experience working with large-scale structured and unstructured datasets. • Understanding of automotive signals, ECUs, CAN, Ethernet, and embedded systems fundamentals. Nice to Haves • Experience developing or validating AI/ML models for ADAS, Autonomous Driving, Robotics, or Automotive applications. • Experience with perception datasets involving camera, radar, lidar, and vehicle sensor data. • Experience training transformer-based models and foundation models. • Familiarity with MLOps tools including MLflow, Weights & Biases, Kubeflow, Azure ML, or SageMaker. • Knowledge of model explainability, bias analysis, robustness testing, and AI validation methodologies. • Experience with GPU optimization, distributed training, and large-scale data processing frameworks. • Exposure to C/C++, MATLAB, signal processing, or embedded software development. • Experience with CANoe, CANalyzer, SIL/HIL environments, and automotive validation workflows. Why Join Aptiv You grow at Aptiv. Aptiv's winning culture is global by design. We bring together diverse perspectives, cultures, and business contexts to solve our customers' toughest challenges. In this environment, every individual can grow, lead, and make an impact, regardless of background, because inclusion fuels how we innovate and win.  You make an impact at Aptiv. One of the strengths of humanity is our drive to progress, to improve, to achieve more tomorrow than we did yesterday. People need solutions they can trust when it matters most. At Aptiv, you are building those solutions every day. You have support at Aptiv. We ensure you have the resources and support you need to take care of your family, your physical health, and your mental health with a competitive benefits package. Your Benefits • Higher Education Opportunities (UDACITY, UDEMY, COURSERA are available for your continuous growth and development); • Life  and accident insurance; • Sodexo cards for food and beverages • Well Being Program that includes regular workshop