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# AI Test Systems Engineer, Tesla AI

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $118,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, Automation of Tests, Bash Shell, C++ (Programming Language), Continuous Integration, Software Debugging, Linux, Distributed Systems, Firmware, Python (Programming Language), Object-Oriented Software Development, Software Engineering, Test Data, Multithreading, Scripting, Delivery Pipeline, Large Language Models, Backend, Containerization, Data Analytics, Teamcity, Docker, Jenkins, Artifactory
- **Published:** July 26, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/17704728?backUrl=%2Fcareer%2F17704728%2FAi-Test-Systems-Engineer-Tesla-Ai-California-Palo-Alto

## About the Role

* Strong foundationin software engineering with a focus on system design, object-oriented programming, distributed systems, and scalable backend architectures
 * Advancedproficiencyin Python, including object-oriented design, multi-threading, and scripting; experience with C++ a plus
 * Deep hands-on experience building and owning HIL and/or SIL testing frameworks, witha track recordof solving complex validation challenges
 * Hands-on experience with containerization technologies such as Docker or Singularity and CI/CD tools like Jenkins, TeamCity, or Artifactory
 * Comfortable working in Linux/Unix environments withexpertisein Bash, scripting, and low-level system debugging
 * ExperienceleveragingAgentic tooling in production systems
 * A relentless drive to improve quality without sacrificing velocity, with a proven ability to own end-to-end solutions from concept to deployment
 * Hands-on experience with simulation frameworks for autonomy or robotics-building or scaling simulation infrastructure, scenario generation, synthetic data, or sim-to-real validation

## Description

* Build scalable infrastructure-as-code solutions to automate CI/CD test workflows, from scheduling to execution, ensuring rapid iteration and high reliability
 * Design systems thatleverageAI/ML toidentifyedge cases,optimizetest scenarios, and prioritize safety-critical failures
 * Build scalable systems to aggregate, visualize, and extract actionable insights from petabytes of test data across diverse test variants, enabling teams to make data-driven decisions
 * Collaborate closely with Autonomy and firmware teams to embed intelligent test frameworks directly into development pipelines, reducing bottlenecks and accelerating deployment
 * Integrate LLM-based tooling to automate root-cause analysis of test failures, turning hours of manual debugging into instant insights


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