Software Data Engineering & Scientist Internship (Summer 2023)

Tesla

  • Internships

Responsibilities

Disclaimer: This position is expected to start in-person around May/June 2023 and continue through the entire Summer term (i.e., through Aug/Sep 2023). We ask for a minimum of 12 weeks, full-time, for all internships. Please consider before applying.

International Students: If your work authorization is through CPT, please consult your school on your ability to work 40 hours per week before applying. Again, please do not apply until you know you can work 40 hours per week. This is required for external applicants. Many students will be limited to part-time during the academic year.

Internship Program at Tesla

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

Locations

  • Fremont, CA
  • Austin, TX

About the Team

Intern will utilize large-scale data and help Tesla engineers design and validate the most compelling and reliable products for our customers. The reliability data team collects real-time life data from test and fleet (energy, charging, and vehicle products) and is responsible for retrieving, analyzing and summarizing results to cross-functional teams. The team provides support through the whole design cycle by building software and statistical tools that orchestrate all the reliability physics analyses.

Requirements

Currently working towards Bachelor’s degree or higher in quantitative discipline (e.g. Statistics, Computer Science, Mathematics, Physics, Electrical Engineering, Industrial Engineering) or the equivalent in experience and evidence of exceptional ability 

Excellent Software skills (proficiency in Python – Data Science stack) 

Strong DevOps skills 

Strong knowledge of data structures, architectures, and languages such as SQL Solid understanding of statistics 

Strong verbal and written communication skills 

Nice to have 

Experience with Machine Learning & time-series modeling PySpark and Big Data frameworks 

Familiarity with CI/CD 

Ability to code robust apps (potentially interfacing with data streams, etc.)