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
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.
About the Role
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:
- State Estimation and Sensor Fusion: Combine information from multiple sensors and vehicle signals to estimate position, orientation, motion, and other states robustly.
- Sensor Alignment and Calibration: Develop methods for static and dynamic calibration, sensor registration, and maintaining alignment as vehicle and environmental conditions change.
- Localization: Improve real-time localization and map-based positioning across diverse driving environments.
- Next-Generation Mapping: Build or evaluate high-fidelity, large-scale maps that provide useful spatial context beyond the immediate sensor range.
- Fallback and Secondary Capabilities: Develop reliable estimation, localization, or mapping functions that support safe operation when nominal system capabilities are unavailable or degraded.
What You’ll Do
- Develop, prototype, and evaluate algorithms for sensor fusion, state estimation, localization, calibration, mapping, or related autonomy capabilities.
- Work with camera, lidar, radar, inertial, vehicle, map, and other multimodal data sources.
- Implement software and tools for data processing, visualization, offline analysis, simulation, and system evaluation.
- Analyze accuracy, robustness, latency, and failure modes across diverse operating conditions.
- Apply software engineering best practices, including design reviews, code reviews, automated testing, continuous integration, and documentation.
- Collaborate with perception, planning, controls, robotics, systems engineering, and vehicle-program partners.
- Communicate technical findings and contribute to design discussions and project documentation.
Required Qualifications
- Currently enrolled in or pursuing a Masters degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, Applied Mathematics, or a related technical field.
- Demonstrated experience through coursework, research, academic projects, or professional work in robotics, state estimation, sensor fusion, computer vision, localization, mapping, controls, or a related area.
- Strong programming skills in C++ and/or Python.
- Strong foundation in linear algebra, probability, geometry, optimization, or estimation methods.
- Strong analytical and problem-solving skills.
- Ability to work collaboratively in a cross-functional, team-oriented environment.
- Strong written, verbal, and presentation skills.
- Availability to work full-time, 40 hours per week, during the internship period.
Preferred Qualifications
- Experience with Kalman filtering, factor graphs, SLAM, visual-inertial odometry, bundle adjustment, point clouds, geometric computer vision, or multi-sensor calibration.
- Familiarity with autonomous vehicles, advanced driver assistance systems, robotics, ROS, or safety-critical systems.
- Experience with large-scale sensor or map datasets, simulation, data association, or offline evaluation.
- Experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX.
- Familiarity with real-time systems, distributed systems, high-performance computing, or production software development
- Intent to return to a degree program after completion of the internship or co-op.
- Must be graduating between December 2027 and June 2028
Compensation:
- The monthly salary range for this role is $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.
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We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. 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.
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