Machine Learning Engineer at Aptiv

Shanghai, Shanghai, China

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AutomotiveIndustries

Requirements

Proficiency in supervised learning, unsupervised learning, and reinforcement learning techniques
Expertise in model evaluation, validation, performance metrics analysis, accuracy, reliability, and robustness testing
Knowledge of automated machine learning (AutoML) pipelines, frameworks, tools, feature engineering, model selection, and hyperparameter tuning
Experience with cloud computing platforms such as AWS, Azure, or Google Cloud for deployment, scaling, data storage, processing, and analysis
Understanding of continuous integration and continuous development (CICD) pipelines and practices
Familiarity with automotive product development processes and industry standards
Skills in designing and conducting A/B tests and experiments, including data-driven analysis and recommendations
Proficiency in Kubeflow Pipelines for ML workflows in Kubernetes environments
Experience with Jenkins for continuous integration and delivery pipelines

Responsibilities

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

Skills

Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

AutoML

AWS

Azure

Google Cloud

CI/CD

Kubeflow

Kubernetes

A/B Testing

Feature Engineering

Hyperparameter Tuning

Aptiv

Develops integrated systems for vehicles

About Aptiv

Aptiv develops integrated vehicle systems for the automotive industry, focusing on software-defined vehicles. Their products help tackle challenges related to autonomous driving, vehicle electrification, and advanced safety features. Aptiv collaborates with major car manufacturers and other mobility stakeholders to enhance vehicle performance and safety through their technological solutions. Unlike many competitors, Aptiv emphasizes a partnership model, providing ongoing support and integration of their systems into vehicles. The company's goal is to improve the overall driving experience and safety in vehicles while advancing the future of mobility.

Dublin, IrelandHeadquarters

1994Year Founded

$7.8MTotal Funding

IPOCompany Stage

Automotive & TransportationIndustries

10,001+Employees

Risks

Amazon's entry into connected cars increases competition for Aptiv.

Investment in MAXIEYE exposes Aptiv to geopolitical and regulatory risks.

Oragadam plant expansion may strain finances if demand doesn't meet expectations.

Differentiation

Aptiv integrates software and systems for autonomous driving and vehicle electrification.

The company collaborates with major car manufacturers for software-defined vehicle solutions.

Aptiv operates 14 technical centers globally, enhancing its R&D capabilities.

Upsides

Aptiv's investment in MAXIEYE expands its presence in the Chinese automotive market.

The Oragadam plant expansion increases production capacity for advanced vehicle systems.

Participation in CES 2025 showcases Aptiv's leadership in automotive technology innovation.

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