Machine Learning Engineer
At a glance
Mid-level Machine Learning Engineering role at Aptiv. Shanghai · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and test machine learning models for AI applications, using supervised, unsupervised or reinforcement methods
- Review model metrics and validation results to check accuracy, reliability and robustness
- Automate feature creation, model choice, tuning, training and deployment with AutoML workflows
- Deploy and scale models on cloud platforms, using cloud resources to support ML data workflows
- Create ML workflows with Kubeflow Pipelines and Kubernetes, and automate project delivery tasks with Jenkins
What you bring
- You bring knowledge of automotive product development and industry standards for building AI/ML solutions
- You have hands-on experience with Kubeflow Pipelines and Kubernetes for orchestrating ML workflows
- You have used Jenkins to automate build, test and deployment work for AI/ML projects
Who this fits
This role suits a mid-level machine learning engineer applying models and automated workflows to automotive applications. The position is based in Shanghai, China.
From the employer
Major Accountabilities:
- Develop and evaluate machine learning models for various AI applications, leveraging techniques such as supervised learning, unsupervised learning, and reinforcement learning.
- Conduct thorough model evaluation and validation, including performance metrics analysis, to ensure accuracy, reliability, and robustness.
- Implement and optimize automated machine learning (AutoML) pipelines and frameworks to streamline model development, training, and deployment processes.
- Utilize AutoML tools and techniques to automate feature engineering, model selection, and hyperparameter tuning for improved efficiency and scalability.
- Utilize cloud computing platforms such as AWS, Azure, or Google Cloud to deploy and scale machine learning models and applications.
- Leverage cloud-based services and resources for data storage, processing, and analysis to support AI/ML workflows and pipelines.
- Implement continuous integration and continuous development (CICD) pipelines and practices to automate model deployment, testing, and monitoring.
- Integrate AI/ML solutions into existing CICD workflows, ensuring seamless integration with software development processes.
- Apply knowledge of automotive product development processes and industry standards to design and develop AI/ML solutions for automotive applications.
- Collaborate with cross-functional teams to understand product requirements, specifications, and constraints, ensuring alignment with automotive development practices.
- Design and conduct A/B tests and experiments to evaluate the effectiveness and impact of AI/ML models and algorithms in real-world scenarios.
- Analyze test results and make data-driven recommendations for model improvements and optimizations based on observed performance metrics.
- Develop and orchestrate ML workflows using Kubeflow Pipelines to automate model training, evaluation, and deployment processes in Kubernetes environments.
- Implement continuous integration and delivery pipelines using Jenkins to automate build, test, and deployment tasks for AI/ML projects.
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Aptiv is an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender identity, sexual orientation, disability status, protected veteran status or any other characteristic protected by law.