Internship, RL Engineer, Tesla SI (Fall 2026/Winter 2027)

What to Expect

Consider before submitting an application:

This position is expected to start August or September 2026 and continue through fall term (ending approximately December 2026) or continuing into Winter/Spring 2027 if available and there is an opportunity to do so. We ask for a minimum of 12 weeks, full-time (40 hours/week) and on-site, for most internships. Our internship program is for students who are actively enrolled in an academic program. Recent graduates seeking employment after graduation and not returning to school should apply for full-time positions, not internships.

International Students: If your work authorization is through CPT, please consult your school on your ability to work 40 hours per week before applying. You must be able to work 40 hours per week on-site. Many students will be limited to part-time during the academic year.

The Internship Recruiting Team is driven by the passion to recognize and develop emerging talent. Our year-round program places the best students in positions where they will grow technically, professionally, and personally through their experience working closely with their Manager, Mentor, and team. We are dedicated to providing an experience that allows the intern to experience life at Tesla by including them in projects that are critical to their team’s success.

About the Team:

Tesla is on a path to build humanoid bi-pedal robots at scale. As a member of the software team, you will design and implement reliable & efficient code for learned robotics, specifically applying to precise manipulation, full body locomotion, and more. You will be responsible for end-to-end robotic learning and own this stack from inception to deployment. Most importantly, you will see your work repeatedly shipped to and utilized by thousands of humanoid robots in real world applications.

What You’ll Do

• Develop end-to-end robotic learning with either reinforcement or imitation learning
• Train machine learning and deep learning models on a computing cluster for grasping and locomotion tasks
• Learn to perform dexterous tasks using high degree of freedom hands
• Learn different robot policies to solve language-conditioned tasks from vision

What You’ll Bring

• Currently pursuing a Degree in Computer Science, Robotics, Machine Learning, Controls, or a related field with a graduation date of (2026-2027)
• Proficiency in Python (numpy, pytorch) and solid software fundamentals
• Strength in at least one of: reinforcement learning; deep learning, including network architectures and training recipes; or robotics and controls, including dynamics, kinematics, and optimization-based control
• Preferred: legged or dynamic robots, sim2real, or massively parallel simulation (e.g. mjlab, Isaac Lab); impedance or force control, MPC, or trajectory optimization; designing or tuning network architectures and training recipes