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Aptiv

Automotive electronics and software platform provider

Senior Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • A Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, Computer Engineering, or a related discipline is required.
  • Six to eight years of industry experience in Machine Learning, Data Science, Deep Learning, or related fields is required.
  • Strong Python development skills and hands-on experience building production-grade machine learning pipelines are required.
  • Demonstrated experience training Machine Learning and Deep Learning models from scratch using large-scale datasets is required.
  • Hands-on experience with dataset creation, data collection, labeling, cleaning, augmentation, feature engineering, and data quality assessment is required.
  • Strong understanding of statistical learning, model evaluation metrics, validation methodologies, and error analysis is required.
  • Experience working with large-scale structured and unstructured datasets is required.
  • Understanding of automotive signals, ECUs, CAN, Ethernet, and embedded systems fundamentals is required.

Responsibilities

  • Design, develop, train, fine-tune, and validate Machine Learning and Deep Learning models for ADAS and automotive validation use cases.
  • Build end-to-end ML pipelines for 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, using distributed training techniques when required.
  • Perform hyperparameter tuning, model optimization, performance benchmarking, and error analysis.
  • Develop evaluation frameworks and validation methodologies for machine learning model performance across different operating conditions.
  • Collaborate with algorithm and validation teams to investigate model failures and improve model robustness.
  • Create reproducible machine learning 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 to ECU to analytics to validation.
  • Participate in hands-on vehicle testing and data collection under the 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 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.
  • Work closely with the 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.

Desired Qualifications

  • 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.

About the Company

Aptiv

Automotive electronics and software platform provider

Aptiv provides the electronic brain and nervous system for modern vehicles, powering features from active safety to software-defined vehicle architecture. Its products sit in the car’s electronics stack, enabling connectivity, advanced safety, electrification, and autonomous capabilities through computer modules, software, and embedded systems. Unlike traditional auto parts suppliers, Aptiv focused its business on high-tech automotive electronics and software, after spinning off its legacy powertrain unit in 2017 and rebranding from Delphi. It expands its capabilities through strategic acquisitions such as nuTonomy (self-driving tech) in 2017 and Wind River (edge software) in 2022, and created Motional, a joint venture with Hyundai to commercialize autonomous driving. Aptiv’s goal is to be a leading technology partner for automakers by delivering the core intelligence for next-generation vehicles, including safety, connectivity, electrification, and autonomous driving.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1994

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