2026 Summer Intern – Software Engineer, Autonomous Robot (PhD)

To helpfacilitateadministration of relocation benefits if you are selected please apply using the permanent address you would move from.

Work Arrangement:
Hybrid: This internship is categorized ashybrid. The selected intern is expected to report to the office up to three times per week or asdeterminedby the team.

Locations:

Mountain View California

Sunnyvale California

We areseekinghighly motivated interns to research explore and evaluatecutting-edgeAI-driven approaches for robot localization/map constructionperception 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 capabilitiesfromperceptionand planning to control and system integrationbringinginnovative real-world autonomous solutions to the future of the work.

About the Role:

We are looking for a self-motivated intern to prototype the development of AI-driven sense-plan-act architecture that supports the development testing and validation of autonomous robotic systems in manufacturing 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 toaccomplishthe technical and businessobjectives.

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 Birds 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

Expertisein 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 birds eye view (BEV) semantic representation

• Motion path planning algorithms e.g. Nav2

• Simulation engines: e.g.IsaacSimIsaacLab 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 DETRBEVformerBEVfusion 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 $10400 - $12300 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.

Whatyoullget 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