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# Software Engineer, AI Inference CoDesign

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $140,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Artificial Neural Networks, C++ (Programming Language), Computer Clusters, Nvidia CUDA, Python (Programming Language), Machine Learning, OpenCL, AI Infrastructure, Application Specific Integrated Circuits, Pytorch, Information Technology, Deployment Automation
- **Published:** September 5, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/15751532?backUrl=%2Fcareer%2F15751532%2FSoftware-Engineer-Ai-Inference-Codesign-California-Palo-Alto

## About the Role

* Proficiency with Python and C++, including modern C++ (14/17/20)
 * Proficiency with PyTorch or another machine learning framework
 * Proficiency with training and deploying neural networks for real-world AI
 * Proficiency with computer systems and computer architecture
 * Experience with CUDA

## Description

Our team productionizes and experiments with novel ML models for our custom HW- we train and deploy large neural networks for efficient inference on compute-constrained edge devices (CPU / GPU / AI ASIC). The nature of this role is multi-disciplinary - you will work at the intersection of machine learning and systems by building the co-designing ML models and their developing the associated deployment infrastructure that enables the seamless training, deployment, and inference of all neural networks that run at Tesla AI.
What You'll Do

 * Build robust AI frameworks to lower neural networks to edge devices
 * Build robust AI infrastructure to train and fine-tune networks for Autopilot and Optimus on large GPU clusters
 * Deploy state-of-the-art neural networks on heterogenous compute, including Tesla's in-house AI ASIC, with an aim to maximize network performance while minimizing latency
 * Collaborate with AI scientists and compiler engineers to effectively compress large models to run in low precision
 * Design and implement custom GPU kernels (CUDA / OpenCL) for efficient training and post-processing of network outputs


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