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# Software Engineer, Frontier SI Infrastructure

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $176,000.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Nvidia CUDA, Software Debugging, Distributed Systems, Python (Programming Language), Machine Learning, Graphics Processing Unit (GPU), High Performance Computing, Pytorch, Deployment Automation, Machine Learning Operations, Data Pipelines
- **Published:** October 15, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/87485541/1

## About the Role

* Strong software engineering fundamentals in Python and
C++

 * Experience with distributed systems, high-performance
computing, or large-scale infrastructure

 * Understanding of machine learning fundamentals, including
optimization, training dynamics, and evaluation

 * Familiarity with PyTorch and modern deep learning
frameworks

 * Ability to analyze performance bottlenecks using
profiling and observability tools

 * Strong debugging and problem-solving skills
 * Excellent communication and collaboration skills

## Description

We are looking for strong software engineers to helpscale the next generation of large AI models for Autopilot, Optimus &Digital Optimus. This role sits at the intersection of distributed systems,machine learning, and performance engineering. You will work closely with MLpractitioners and infrastructure engineers to improve training efficiency,accelerate experimentation, and enable larger and more capable models.

The ideal candidate is excited about both systems andmachine learning. You should be comfortable debugging distributed trainingissues, analyzing model behavior, and using data to demonstrate howinfrastructure improvements translate into better model quality.

You will help build and optimize large-scale trainingsystems running on thousands of GPUs while developing the tools, metrics, andworkflows needed to make model scaling faster and more predictable.
What You'll Do

 * Optimize large-scale distributed training across
thousands of GPUs

 * Improve training throughput, utilization, reliability,
and scalability

 * Develop tooling to identify bottlenecks in compute,
networking, memory, and data pipelines

 * Design and implement performance optimizations across
PyTorch, CUDA, communication libraries, and training frameworks

 * Partner with researchers to evaluate how infrastructure
changes impact model quality, convergence, and downstream metrics

 * Analyze training runs and build dashboards that connect
system performance to model outcomes

 * Drive improvements in model scaling efficiency, including
larger models, longer context lengths, and higher-quality datasets

 * Debug complex issues across software, hardware,
networking, and machine learning systems

 * Build infrastructure that accelerates experimentation and
shortens iteration cycles for researchers