Software Engineer Intern - SI Reinforcement Learning

Job Description

<p>Consider before submitting an application:     </p><p>This position is expected to start May 2027 and continue through Summer term (ending approximately August 2027) or continuing into Fall 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.    </p><p>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.   </p><p>Internship Program at Tesla  </p><p>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.</p><p>About the Team: </p><p>As a Software Engineer Intern on Tesla SI’s Reinforcement Learning Infrastructure team, you will help build the systems that make large-scale reinforcement learning fast, stable, and flexible for ML engineers working on real-world SI problems. Your work on RL training infrastructure will support scaling Robotaxi and Optimus.  </p><p>We're looking for someone with practical experience or strong project/research exposure in RL training infrastructure. An ideal candidate will also have experience in RL techniques that improve both model quality and training performance. </p><p>Your work will directly impact iteration speed, system reliability, and the ability of ML engineers to move from prototype to large-scale experiments quickly.  </p>