Job Description
To help facilitate administration of relocation benefits if you are selected, please apply using the permanent address you would move from.
Work Arrangement:
Hybrid: This internship is categorized as hybrid. The selected intern is expected to report to the office up to three times per week or as determined by the team.
Locations:
Mountain View, California
Sunnyvale, California
We are seeking highly motivated interns to research, explore, and evaluate cutting-edge AI-driven approaches for robot localization/map construction, perception, motion planning, scenario simulation, and data engineering. The role will involve hands-on experimentation, algorithm development, and integration of multi-modal sensor data to advance autonomous robotic systems.
About the Team:
The Robotics Software team is developing the next generation of autonomous robotic systems, focusing on autonomous mobile robots (AMRs) and intelligent robotic platforms. We develop full-stack robotics capabilities-from perception and planning to control and system integration-bringing innovative, real-world autonomous solutions to the future of the work.
About the Role:
We are looking for a self-motivated intern to proto@type the development of AI-driven sense-plan-act architecture that supports the development, testing, and validation of autonomous robotic systems in manufacturing plants. In this role, you will focus on developing camera- and LiDAR-based wheel-drive robotic system, design technical specification, creating and executing test plan, integrating the software with physical and simulation platforms, and enabling teams to accomplish the technical and business objectives.
You will work cross-functionally with experts in autonomy, contributing to system-level validation and the continuous improvement of system robustness and validation workflows. You will focus on one or more of the following areas:
Localization
Evaluate and test LiDAR-based localization repositories.
Investigate Gaussian splatting localization pipelines and assess feasibility for embedded platforms.
Explore machine-learning techniques for feature point correspondence between image frames.
Implement and benchmark place recognition algorithms using computer vision.
Integrate dynamic object handling into localization workflows.
Develop multi-agent map-building and construction processes (offboard).
Design sensor fusion strategies for heterogeneous modalities (e.g., 3D LiDAR, 2D LiDAR, monocular camera, IMU, wheel odometer).
Apply post-processing optimization algorithms (e.g., factorgraphand posegraph).
Data Engineering
Create, curate, and manage datasets for training AI models.
Ensure data quality and diversity for robust algorithm development.
Simulation
Upgrade the existing simulation environment to support generation of realistic 3D LiDAR data and photorealistic image rendering for advancedperceptiontesting.
Design and implement adversarial scenarios toidentifypotential safety vulnerabilities and enhance overall system robustness.
Perception
Developperceptionsolutionsleveragingjoint representation of Bird's Eye View (BEV) and DETR-based object detection using multi-modality inputs.
Enhance robustness inperceptionpipelines for dynamic environments.
Motion Planning
Research and implementdenoisingdiffusion-based motion planning algorithms.
Reinforcement learning in simulation engine to improve path generation policy.
Evaluate performance and scalability of AI-driven planning approaches in real-world scenarios.
Key Responsibilities :
Design and implement high-precision localization methods using camera, LiDAR, wheel encoder and inertial sensors.
Develop scalable and real-time localization moduleoptimizedfor autonomous robotic systems.
Create engineering specifications and test procedures to ensure system compliance.
Evaluate and benchmark the performance of systems.
Review the state-of-the-art in camera- and LiDAR-based algorithms
Troubleshoot using strong knowledge of probabilistic estimation, sensor fusion, and real-time system implementation.
Adjust and fine-tune system parameters to improve accuracy and robustness
Required Qualifications:
HasaMastersDegree andis currentlyenrolled in a PhD programin Robotics, Computer Science, Electrical/Mechanical Engineering, or related technical fields.
Proficiencyin C++ or Python.
Adhere to continuous development and deployment practices in robotic software development
Expertise in one or more of the technical areas:
Camera- and LiDAR-based localization algorithms, statistical estimation theory, and practices such as pose graph and factor graph optimization and implementation.
Understandingstate-of-the-artsolutions inplacerecognition for addressing loop-closure detection issues.
Perception, e.g., feature embedding, object detection, bird's eye view (BEV) semantic representation
Motion path planning algorithms, e.g., Nav2
Simulation engines: e.g.,IsaacSim,IsaacLab, and etc.
Dataset curation and annotation tools
Experienceoptimizingalgorithm/software to balance performance within resource constraints.
Familiarity with ROS2 or other robotics middleware.
Preferred Qualifications:
Machine learning knowledge and practice experience.
Proficiencywith deep learning frameworks and toolchains likePyTorchand TensorFlow
Familiarity with repositories like DETR,BEVformer,BEVfusion, SAMv2, Ceres Library/GTSAM, ORB-SLAM, VINS-Mono, andetc.
Experience working with cloud-based data collection and data pipeline systems.
AV/ADAS integration or industrial automation experience is a bonus.
Graduating between December 2026 and June 2027.
Compensation:
The monthly salary range for this role is $10,400 - $12,300 dependent upon class status and degree.
GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2026 Student Program.
What you'll get from us (Benefits):
Paid US GM Holidays
GM Family First Vehicle Discount Program
Result-based potential for growth within GM
Intern events to network with company leaders and peers
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