2025 Summer Intern – Advanced Controls Systems for ADAS

Job Description

GM DOES NOT PROVIDE IMMIGRATION-RELATED SPONSORSHIP FOR THIS ROLE. DO NOT APPLY FOR THIS ROLE IF YOU WILL NEED GM IMMIGRATION SPONSORSHIP (e.g., H-1B, TN, STEM OPT, etc.) NOW OR IN THE FUTURE.   

Work Arrangement:  

Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to the Mountain View Technical Center three times per week, at minimum. 

The Team: 

The Advanced Driver Assistance Systems (ADAS) team is at the forefront of developing cutting-edge technologies that enhance vehicle safety, performance, and autonomy. We specialize in designing, testing, and deploying intelligent systems that provide critical assistance to drivers, such as adaptive cruise control, lane-keeping assistance, automated braking, and collision avoidance. Our team works on a wide range of systems, integrating data from sensors like lidar, radar, and cameras to create robust, real-time solutions for autonomous navigation and decision-making.

As part of the ADAS team, you will collaborate with experts in machine learning, control systems, sensor fusion, and data science to develop advanced algorithms for vehicle autonomy. We focus on using both model-based and data-driven approaches to optimize the performance of ADAS functionalities, ensuring safety, reliability, and scalability across diverse driving environments. We emphasize real-time control solutions, hardware-in-the-loop (HIL) testing, and simulation to evaluate and refine the effectiveness of our systems.

Our multidisciplinary team fosters a collaborative environment where cutting-edge research is applied to real-world challenges in the automotive sector. We leverage the latest advancements in machine learning, data science, and control theory to push the boundaries of what’s possible in autonomous vehicle technologies. By joining our team, you will play a pivotal role in shaping the future of mobility, driving innovation in the transition towards fully autonomous vehicles

What You’ll Do:

We are seeking a PhD Advanced Controls Intern with a strong focus on machine learning (ML) and automation to join our innovative research and development team. In this role, you will leverage advanced control theory, reinforcement learning, and predictive modeling to develop intelligent automation solutions for complex systems. You will collaborate with multidisciplinary teams to design, simulate, and implement control strategies that optimize performance, enhance efficiency, and adapt to dynamic environments. The ideal candidate is pursuing a PhD in Control Systems, Robotics, Machine Learning, or a related field and is passionate about applying their expertise to solve real-world challenges in automation and intelligent systems.

Key Responsibilities:

• Develop advanced control algorithms leveraging machine learning and reinforcement learning to optimize the performance of autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS)

• Design, simulate, and validate control strategies for path planning, trajectory optimization, and decision-making in dynamic traffic environments

• Analyze sensor data, including lidar, radar, and cameras, to enhance vehicle perception and integrate it into intelligent control systems

• Collaborate with cross-functional teams to implement real-time control solutions for autonomous navigation and adaptive vehicle behavior

• Conduct system-level simulations to evaluate the robustness and scalability of control strategies under varying operational conditions

• Research and apply predictive modeling techniques to anticipate and mitigate potential safety risks in autonomous and ADAS functionalities

• Support hardware-in-the-loop (HIL) and software-in-the-loop (SIL) testing to ensure seamless integration of advanced controls into vehicle systems

• Stay updated on the latest advancements in control theory, machine learning, and autonomous vehicle technologies, incorporating innovative approaches into ongoing projects

• Document methodologies, experimental results, and key insights to support knowledge sharing and collaboration within the team

Additional Job Description

Required Qualifi