2027 Summer Intern - Machine Learning Engineer, Embodied AI

Generalmotors; Sunnyvale (CA); Híbrido; USD 107 000 - 132 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Paid holidays

Vehicle discount

Growth opportunities

Intern events

Mentorship

Resumo da oferta

General Motors is seeking a Software Engineering Intern for its Mapping Engineering team in Sunnyvale, CA. You will work on map data, geospatial algorithms, and pipeline quality with a mentor and cross-team collaboration.

You’ll write Python and/or C++ code, explore road-network concepts, and help validate map data while learning how prototypes become dependable systems. Hybrid onsite schedule supports collaboration.

Qualificações

• Pursuing a bachelor’s or master’s degree in CS, EE, robotics, GIS, or related field.
• Experience programming in Python or C++ through coursework or projects.
• Familiarity with version control, testing, and writing readable code.
• Foundational understanding of data structures and algorithms.
• Interest in geospatial systems, robotics, autonomous vehicles, CV, or ML.
• Ability to communicate technical ideas and work collaboratively.

Responsabilidades

• Contribute to a mapping/geospatial project with mentor support.
• Write, test, and document Python and/or C++ code for geometry processing or map data.
• Explore road-network concepts like lanes, intersections, boundaries, and connectivity.
• Analyze sensor-derived map data and assess data quality.
• Prototype or evaluate algorithms for feature extraction, map change detection, or map quality checks.
• Build tests, validation tools, metrics, and visualizations for map data or software.
• Share progress and final demo with mentor and teammates.
• Participate in code reviews, design discussions, and team learning activities.

Conhecimentos

Python

C++

Version control

Testing

Data structures

Algorithms

Geospatial systems

Communication

Formação académica

BS/MS in CS/EE/Robotics/GIS

Ferramentas

GeoJSON

OpenStreetMap

GeoPandas

GDAL/OGR

PostGIS

Descrição da oferta de emprego

Job Description

Work Arrangement:

Hybrid: This internship is categorized ason-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

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us

We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.

Benefits Overview

From day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.

Non-Discrimination and Equal Employment Opportunities (U.S.)

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.

Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

Accommodations

General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, emailus or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.