AI/ML Mapping Engineer – Geospatial & Cloud

General Motors; Warren (MI); On-site; USD 22,000 - 33,000; Full time

Benefits offered by this job

Paid US GM Holidays

GM Family First Vehicle Discount

Growth opportunities and intern events

Job summary

General Motors is seeking a student intern for the Map Crowdsourcing and Enhancements team in Warren, Michigan. You will tackle complex mapping problems using machine learning, geospatial analytics, and large-scale data processing, contributing from data exploration to model deployment and evaluation.

You will work on cloud-based environments, including Azure and Google Cloud, develop data workflows with PySpark, and build scalable pipelines while learning and growing through structured training

Qualifications

• Currently enrolled in a full-time, degree-seeking program and working toward a Masters or PhD in computer science or related field.
• Able to work full-time, 40 hours per week.
• Proficiency in Python and ML libraries such as PyTorch or TensorFlow.
• Experience with cloud environments and data platforms (Azure, Azure Databricks, GCP).
• Strong fundamentals in statistical modeling and ML frameworks/tools.

Responsibilities

• Build, deploy, and optimize models and algorithms using cloud-based compute environments (Azure Databricks, GCP).
• Develop scripts to manage data workflows using Python or PySpark.
• Analyze large datasets to identify trends and patterns.
• Present complex information using data visualization techniques.
• Create and maintain technical documentation.
• Complete training, learning, and skills development.

Skills

Python

ML libraries

PyTorch or TensorFlow

Cloud platforms

SQL / Spark

Education

Masters or PhD in computer science or related field

Tools

Azure Databricks

Microsoft Azure

Google Cloud Platform

Job description

General Motors is seeking a student intern for the Map Crowdsourcing and Enhancements team in Warren, Michigan. You will tackle complex mapping problems using machine learning, geospatial analytics, and large-scale data processing, contributing from data exploration to model deployment and evaluation.

You will work on cloud-based environments, including Azure and Google Cloud, develop data workflows with PySpark, and build scalable pipelines while learning and growing through structured training