Internship, Robotaxi Simulation Engineer, Systems Modeling & Optimization (Fall 2026)

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 later, if available). 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. About the Team: As a member of the Systems Modeling & Optimization team, you will work on Tesla's robotaxi simulation platform, a high-fidelity model used to validate and develop fleet-management algorithms before production rollout. The simulation serves as a sandbox for the algorithms that manage day-to-day operations of Tesla's Robotaxi fleet. You will contribute to two critical areas: developing next-generation fleet orchestration algorithms and improving the simulation engine's runtime and memory performance to support larger fleets and longer scenarios. You will apply skills in optimization, operations research, and software engineering to problems that directly shape the Robotaxi customer experience.

Responsibilities

Design and implement optimization algorithms for fleet orchestration problems including matching, repositioning, and charging Build automated validation pipelines that compare simulation outputs against production data to detect model drift and regressions Profile and optimize simulation hot paths, targeting significant runtime and memory improvements Implement memory optimizations to support extended multi-day scenario analysis at scale Establish performance benchmarks and integrate them into CI to prevent regressions Collaborate to align simulation algorithms with production services Communicate analysis results and algorithm tradeoffs to cross-functional teams and decision makers

Requirements

Currently pursuing a degree in Computer Science, Operations Research, Engineering, or a related field with a graduation date between December 2026 and December 2027 Fluency with exploratory statistical analysis and testing on large data sets Strong fundamental background in optimization techniques (linear/nonlinear programming, combinatorial optimization, stochastic modeling) applied to fleet management or logistics problems Strong evidence of ability to design, analyze, and implement optimization schemes for real-world systems from the ground up Experience with performance profiling and optimization of Python applications (memory management, CPython extensions, or similar) Proficiency in Python; experience with Golang and collaborative code development is a plus Strong evidence of ability to collaborate cross-functionally, work hands-on, and execute on open-ended projects in a fast-paced environment