Internship, System Controls Engineer, Energy Engineering (Winter/Spring 2027)

What to Expect

Consider before submitting an application:

This position is expected to start January 2027 and continue through the entire Winter/Spring term (i.e through April/May 2026) or into Summer 2027 if available. We ask for a minimum of 12 weeks, full-time (40 hours/week) and on-site, for most internships.

International Students: If your work authorization is through CPT, please consult your school before applying. You must be able to work 40 hours per week. 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.

The Tesla Energy Products team is bringing battery and solar technology to the grid to facilitate a renewable energy ecosystem and redefine the way the world will use energy in the future. As a System Controls intern in the Controls team, you will support modelling, development, and implementation of innovative and cutting-edge estimation and control algorithms in our residential and industrial battery systems.

Location: Palo Alto, CA

What You’ll Do

• Support research and development of advanced optimal control algorithms for industrial and residential battery energy storage products
• Help create and integrate models, define algorithms, write testing code, and evaluate the performance of algorithms throughout the life of the product
• Present your findings to the broader engineering teams

What You’ll Bring

• Currently pursuing a graduate degree in an engineering discipline, math, physics or a related field
• Research experience in optimal control methods including but not limited to: MPC, LQR, trajectory optimization, learning based control
• Familiar with linear systems analysis, state estimation, and parameter identification methods
• Experience with Python and/or MATLAB/Simulink
• Ability to communicate efficiently and collaborate cross-functionally
• Experience in gradient-based optimization methods is a plus
• Experience in differentiable programming is a plus
• Good fundamentals of thermodynamics and heat transfer is a plus
• Familiar with electrothermal modeling of lithium-ion battery cells is a plus
• Familiar with uncertainty quantification methods is a plus