Planning and Control Adaptation Intern

Join NVIDIA's autonomous driving team as a Planning and Control Adaptation Intern. You will work with experienced engineers to support Planning and Control integration and adaptation across multiple vehicle platforms, while gaining hands-on experience in issue analysis, algorithm validation, and engineering tool development.

Responsibilities:

• Support Planning and Control integration, parameter tuning, and adaptation across vehicle platforms with different dynamics, steering and braking systems, and actuator configurations.
• Analyze driving issues involving path and speed planning, lane changes, merging, yielding, intersections, and lateral and longitudinal control.
• Use simulation, replay tools, and vehicle logs to reproduce issues, investigate root causes, and validate improvements.
• Perform cross-carline testing and evaluate driving safety, comfort, smoothness, and robustness.
• Develop Python or C++ tools for log analysis, parameter comparison, regression testing, and result visualization.
• Collaborate with Planning and Control engineers on integration, validation, and delivery for production autonomous driving programs.

Requirements:

• Currently pursuing a Master's or PhD degree in Computer Science, Robotics, Automotive Engineering, Electrical Engineering, or a related field.
• Good C++ or Python programming skills, with familiarity with Linux development and software debugging workflows.
• Basic understanding of motion planning, vehicle control, vehicle kinematics or dynamics, or numerical optimization, with strong analytical, problem-solving, and teamwork skills.
• Proficient English communication skills, both written and verbal, with the ability to collaborate effectively with global teams.
• Able to work at least three days per week throughout the internship.

Nice to Have:

• Research, internship, or competition experience related to autonomous driving or robotics.
• Familiarity with path planning, trajectory optimization, MPC, PID, A, Hybrid A, or related algorithms.
• Experience with ROS, Apollo, Autoware, CARLA, CarMaker, or similar platforms.
• Experience with vehicle logs, CAN signals, simulation, parameter tuning, or engineering tool development.