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# Software Engineer, AI Hardware Infrastructure

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $140,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Debugging, Python (Programming Language), Linux System Administration, Software Engineering, High Performance Computing, Reliability of Systems, Information Technology, Hardware Acceleration
- **Published:** September 5, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/85558805/1

## About the Role

* Degree in Engineering, Computer Science, or equivalent experience with evidence of exceptional ability and practical software engineering expertise

 * Strong proficiency in Python and adaptability to learn new languages and frameworks

 * Extensive familiarity with Linux administration and internals

 * Experience or strong interest in automation, observability, and infrastructure development and deployment

 * Ability to collaborate effectively with cross-functional teams to debug and optimize complex systems

## Description

As a member of the AIHW Infra team, you will play a critical role in supporting Tesla's AI hardware initiatives by developing automation, infrastructure, and services. Join a dynamic team of engineers dedicated to accelerating workloads through collaboration with AI HW design teams and High-Performance Computing (HPC) groups. Your primary focus will be building robust infrastructure solutions, while also assisting in debugging performance bottlenecks and root-causing cluster issues as needed. The ideal candidate is a proactive engineer with a passion for creating scalable, efficient systems.
What You'll Do

 * Developing Python libraries to automate, monitor, measure, and troubleshoot workflows on AI hardware infrastructure

 * Spending approximately 50% of time building automation and infrastructure, primarily in Python and other languages, with the remaining time focused on debugging, experimentation, and resolving infrastructure challenges and performance bottlenecks

 * Creating and maintaining tools for infrastructure, automation, observability, and reporting to ensure system reliability and performance

 * Collaborating with AI HW design and HPC teams to identify, debug, and resolve performance bottlenecks and cluster issues

 * Supporting internal users by triaging errors, root-causing issues, and providing effective, maintainable solutions

 * Proactively addressing potential infrastructure challenges to minimize user impact and enhance scalability and efficiency of AI hardware workloads


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