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# AI Infrastructure Engineer, Network Deployment & Inference, Optimus

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $176,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Artificial Neural Networks, Computer Programming, Python (Programming Language), Machine Learning, Robotic Automation Software, AI Infrastructure, Supervised Learning, Scripting, Real Time Systems, Delivery Pipeline, Model Validation, AI Platforms, Hardware Infrastructure
- **Published:** September 5, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/13470139?backUrl=%2Fcareer%2F13470139%2FSoftware-Engineer-Machine-Learning-Integration-Optimus-California-Palo-Alto

## About the Role

* Strong programming experience in Python and practical experience programming in C++ software
 * Solid understanding of machine learning fundamentals (e.g., supervised learning, neural networks, model evaluation)
 * Experience with model inference, deployment, and optimization
 * Excellent problem solving, critical thinking, and communication skills

## Description

Tesla AI is solving robust, real-world AI through humanoid robots.As an AI Engineer within our AI teams, you will contribute to one of the most advanced Robotics/AI Platforms in the world.You'llbe responsible forintegrating machine learning models with real-time systems, working across the software stack to ensure seamless communication between neural networks and hardware components.
What You'll Do

 * Integrate ML models into embedded or robotic systems with tight latency and performance constraints
 * Write Python scripts/tools for training, evaluation, and deployment pipelines
 * Collaborate with machine learning researchers to bring models from prototype to production
 * Optimize runtime performance of neural networks on various compute environments (e.g., GPU, CPU, edge accelerators)
 * Design and implement efficient, robust systems for real-time ML inference and control and for the humanoid robot software stack


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