Robotics Intern (Part Time)

Faraday Future (FF) is a California-based embodied artificial intelligence ecosystem company, leveraging the latest technologies and world’s best talent to realize exciting new possibilities in mobility and robotics. We’re producing user-centric, technology-first vehicles and robots to establish new paradigms in human-AI interaction. We’re not just seeking to change how our cars and robots work – we’re seeking to change the way we drive and interact with machines. At FF, we’re creating something new, something connected, and something with a true global impact.

We are looking for a highly motivated Robotics Intern (Part Time) to support robotics control and learning projects. This role is ideal for graduate students who want hands-on experience in robot locomotion, reinforcement learning, simulation, and experimental validation while working closely with a motion control team building real robotic systems.

Responsibilities:

• Support locomotion, motion control, or learning-based robotics projects in simulation and on hardware when appropriate.
• Implement and run reinforcement learning, imitation learning, or control experiments under guidance from the team.
• Build tools for logging, visualization, replay, benchmarking, and experiment analysis.
• Assist with simulator setup, training pipelines, evaluation scripts, and result reporting.
• Help collect, organize, and analyze robot experiment data for controller tuning and performance debugging.
• Contribute to prototype features, research explorations, and internal demos related to motion control and robotics learning.

• Currently pursuing a Master’s or PhD degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, Artificial Intelligence, or a related field.
• Strong Python programming skills, including experience developing and debugging technical or research-oriented code.
• Strong foundation in applied mathematics, including areas such as linear algebra, calculus, probability, optimization, or control theory.
• Hands-on experience or academic research in one or more of the following domains: robotics, reinforcement learning, motion planning and control, optimization, or physics-based simulation.
• Familiarity with robotics development and experimentation workflows, including iterative testing, evaluation, and debugging.
• Strong problem-solving ability and willingness to debug experiments independently.
• Demonstrated analytical and problem-solving skills, with the ability to independently troubleshoot technical challenges and debug experimental results.

• Hands-on experience with PyTorch, reinforcement learning libraries, or imitation learning workflows.
• Familiarity with MuJoCo, Isaac Gym, Isaac Lab, Gazebo, or other robotics simulation environments.
• Hands-on experience with quadruped robots, humanoids, or legged robot simulation/training environments is highly preferred.
• Experience with reinforcement learning training pipelines, policy optimization, or sim-to-real workflows.
• Prior robotics project, lab, or competition experience is strongly preferred.
• Basic C++ knowledge is a plus.
• Ability to document experiments clearly and communicate insights effectively.

($32/hr - $41/hr DOE), plus benefits and incentive plans