Data Engineering Intern, Self-Driving Fleet Data

Job Description

Internship, Data Engineer, Fleet Data, Self Driving | Tesla

The Tone:
This is an Internship position at Tesla, located on-site. Tesla is dedicated to building self-driving technology, leveraging data from millions of cars to improve neural networks and measure performance. This role is highly impactful, contributing directly to critical data pipelines that drive overall goals and priorities for the self-driving team and safety reports.

The TL;DR
• Role: Internship
• Type: Full-time
• Location: In-person, on-site
• Pay: $36000–$50000 yearly
• Team: Self-Driving Fleet Data team, responsible for mining data from Tesla cars to improve neural networks and create real-time analytics.
• Mission: Improve neural networks and measure Self-Driving performance by mining data from millions of Tesla cars and creating real-time analytics.
• Tech Stack: Python, SQL

What You’ll Actually Do
• Develop: Contribute to end-to-end data pipeline development, from fleet data ingestion to visualization, gaining hands-on experience with modern data engineering tools.
• Measure: Help define and implement key performance metrics for Tesla’s self-driving systems, supporting data-driven decision-making through real-world event analysis.
• Monitor: Assist in monitoring system health using observability tools, including log aggregation, alerting workflows, and automated checks to ensure reliability at scale.
• Optimize: Support data flow optimization to improve the volume, diversity, and timeliness of fleet data available to downstream teams.
• Visualize: Create interactive dashboards and visualization tools that translate complex fleet data into clear, actionable insights for cross-functional stakeholders.

The Must-Haves
• Background: Student currently pursuing a Bachelor’s degree in Computer Science, Engineering, Physics, Mathematics, or a related technical field, with a graduation date between December 2026 and December 2027.
• Experience: Practical experience writing clean, efficient Python and SQL code, with exposure to the full data lifecycle including data sourcing, ingestion, transformation, and analysis.
• Skills: Proficiency in Python and SQL; demonstrated ability to take ownership of projects from identifying data needs to building and testing simple pipelines or dashboards; adaptable and proactive in fast-paced, ambiguous environments, comfortable learning new tools, asking questions, and iterating quickly.