Software Engineer Intern - Machine Learning Engineer - Embodied AI

Job Description Work Arrangement: Hybrid: This internship is categorized as on-site. The selected intern is expected to report to the office 3 days a week.

Location: Sunnyvale, CA About the Team Our Mapping organization is building next-generation mapping systems that go beyond static high-definition maps.

We use onboard sensor data, algorithms, and machine learning to help reconstruct and maintain maps at scale. These maps support localization, perception, simulation, and autonomous driving.

The Role We’re looking for a Software Engineering Intern to join our Mapping Engineering team. You’ll work with experienced engineers on a scoped project related to map data, geospatial algorithms, or mapping pipeline quality.

Depending on the project, you may help process sensor-derived data, improve how map features are represented or validated, or evaluate methods for identifying map changes.

You’ll receive guidance from a mentor and collaborate with partner teams such as Perception, Localization, and Simulation. This internship is an opportunity to apply your software engineering skills to real-world mapping challenges while learning how research and prototypes become dependable engineering systems.

What You’ll Do • Contribute to a well-defined mapping or geospatial engineering project, with support from your mentor and team. • Write, test, and document code in Python and/or C++ for tasks such as geometry processing, map data analysis, or pipeline tooling.

• Explore road-network and map concepts, including lanes, intersections, boundaries, connectivity, and map attributes. • Help analyze sensor-derived or third-party map data and investigate data quality or map consistency issues.

• Prototype or evaluate algorithms and machine-learning approaches for feature extraction, map change detection, or map quality checks, as relevant to your project.

• Build or improve tests, validation tools, metrics, and visualizations to help assess the correctness of map data or software. • Share project progress, findings, and a final demo or summary with your mentor and teammates.

• Participate in code reviews, design discussions, and team learning activities. Minimum Qualifications • Currently pursuing a bachelor’s or master’s degree in Computer Science, Electrical Engineering, Robotics, GIS, or a related technical field.

• Experience programming in Python or C++ through coursework, research, internships, or personal projects. • Familiarity with core software engineering practices such as version control, testing, and writing readable code.

• Foundational understanding of data structures and algorithms. • Interest in geospatial systems, robotics, autonomous vehicles, computer vision, or machine learning.

• Ability to communicate technical ideas, ask questions, and work collaboratively. Preferred Qualifications • Coursework, research, or project experience in geospatial computing, computational geometry, robotics, computer vision, machine learning, or graph algorithms.

• Exposure to map or geographic data formats and tools such as GeoJSON, OpenStreetMap, GeoPandas, GDAL/OGR, or PostGIS. • Experience working with camera, lidar, radar, point-cloud, or other spatial data.

• Experience with C++ and Python, or an interest in working across both languages. • Familiarity with machine-learning workflows such as preparing datasets, evaluating models, or building prototypes.

• Prior internship, research, or team-project experience developing software collaboratively. Compensation: • The monthly salary range for this role is $9,600-$11,800 per month • GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2027 Student Program.

What You’ll Get from Us • Paid U.S. GM holidays. • GM Family First Vehicle Discount Program. • Potential for growth within GM based on performance and business needs.

• Intern events and opportunities to network with company leaders and peers. • Mentorship and hands-on experience with the data and ML foundations behind autonomy 



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