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# Software Engineer, Training Performance, AI Infrastructure

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $118,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Artificial Neural Networks, C++ (Programming Language), Computer Clusters, Nvidia CUDA, Computer Programming, Python (Programming Language), Machine Learning, AI Infrastructure, Graphics Processing Unit (GPU), Pytorch, Deep Learning, Parallel Computation, Deployment Automation
- **Published:** September 5, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/11183543?backUrl=%2Fcareer%2F11183543%2FSoftware-Engineer-Training-Performance-Ai-Infrastructure-California-Palo-Alto

## About the Role

* Members of the Autopilot AI Infrastructure team are expected to be adaptable to the dynamic requirements of AI research and capable of contributing across all parts of the AI training software stack
 * Practical experience programming in Python and/or C/C++
 * Experience programming in CUDA, cuDNN or Triton, particularly in the context of operations used in AI workloads
 * Experience profiling and optimizing CPU-GPU interactions (pipelining computation with data transfers, etc.)
 * Experience working with training frameworks (ideally PyTorch)
 * Proficient in system-level software, in particular hardware-software interactions and resource utilization
 * Experience with parallel programming concepts and primitives
 * Understanding of modern machine learning concepts and state of the art deep learning
 * Experience scaling neural network training jobs across many GPUs

## Description

As a Software Engineer within the Autopilot AI Infrastructure team, you will work on reinforcing, optimizing, and scaling our infrastructure components supporting AI research activities for Autopilot and the Optimus.

At the core of our autonomy capabilities are neural networks that the research team is designing to train on very large amounts of data, across large-scale GPU clusters. Robustly training these models at scale and in the shortest amount of time is critical to our mission.
What You'll Do

 * Reduce wall clock time to convergence of our training jobs by identifying bottlenecks in the ML stack, from data-loading up to the GPU
 * Integrate efficient, low-level code with the overall high-level training framework
 * Profile our workloads and implement solutions to increase training efficiency
 * Optimize workloads for efficient hardware utilization (e.g. CPU and GPU compute, data throughput, networking)


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