General Motors
General Motors
Posted 19 days ago
Internship
Sunnyvale, California
Hybrid
Smart Summary
Responsibilities
Develop, prototype, and evaluate algorithms for sensor fusion, state estimation, localization, and mapping within autonomous driving systems. Collaborate with cross-functional teams to implement software tools and analyze system performance across diverse operating conditions.
Qualifications
You are currently pursuing a Master's degree in a relevant technical field and have demonstrated experience in robotics, state estimation, sensor fusion, computer vision, localization, mapping, or controls. You possess strong programming skills in C++ and/or Python, a solid foundation in mathematics, and excellent analytical and problem-solving abilities.
Must Have Skills for ATS
C++
Python
Robotics
State Estimation
Sensor Fusion
Computer Vision
Localization
Mapping
Kalman filtering
SLAM
ROS
PyTorch
TensorFlow
JAX
Job Description
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
The State Estimation, Alignment, and Mapping (SEAM) team develops the sensing and spatial understanding capabilities that help autonomous vehicles know where they are, interpret their surroundings, and maintain reliable operation over time. The team works on sensor fusion, state estimation, real-time localization, static and dynamic sensor calibration, next-generation mapping, and capabilities that support primary and fallback driving systems.
SEAM combines robotics, computer vision, estimation theory, machine learning, geometry, and production software engineering. Our work supports both onboard, real-time autonomy and offboard, high-fidelity map and data-generation workflows.
As a SEAM Intern, you will work on a well-scoped research or engineering project related to sensing, estimation, localization, mapping, or autonomy reliability. You will collaborate with experienced engineers and researchers to design algorithms, implement software, evaluate system performance, and understand how sensing capabilities operate in real-world autonomous driving systems.
Potential focus areas include:
Compensation:
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