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# Fullstack Software Engineer, Machine Learning Platform, AI Infrastructure

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $140,000.0
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Artificial Intelligence, Artificial Neural Networks, Computer Vision, Computer Clusters, Continuous Integration, Relational Databases, Linux, Django Web Framework, Python (Programming Language), Machine Learning, Message Broker, Web Applications, AI Infrastructure, ReactJS, System Availability, Flask (Web Framework), Caching, Backend, Kubernetes, Deployment Automation, React Redux, Data Pipelines, Automation Anywhere
- **Published:** September 1, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/85442407/1

## About the Role

* Strong knowledge of Python, React, and Linux
 * Experience working with backend infrastructure components (relational databases, in-memory caches, message brokers)
 * Experience building modern web applications using Flask/Django and React/Redux or similar component-based libraries
 * Experience deploying services on Kubernetes and setting up CI/CD flows
 * Solid understanding of security principles and best practices
 * UI and graphic design sensibilities
 * Experience working with HPC clusters
 * Knowledge of machine learning, computer vision, or neural networks

## 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.

We are building out the Machine Learning Platform that our engineers and leadership use to schedule, manage and monitor machine learning experiments, data pipelines and artifacts.
What You'll Do

 * Architect and implement scalable, user-friendly tools for AI workflows to track and visualize the lifecycle of machine learning experiments and models
 * Build robust tools and infrastructure to improve the machine learning team's velocity. This includes training and evaluation code in Python to back-end and front-end work in JavaScript
 * Collaborate closely with ML engineers to ensure tools are aligned with research needs
 * Design dashboards to provide real-time insights into performance and progress for our ML engineers and leadership
 * Coordinate required hardware resources with the team managing the cluster hardware to maintain high availability


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