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# Senior Software Engineer - Machine Learning Infrastructure

- **Company:** Commnet Wireless, LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $176,000.0 - $230,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Microsoft Azure, Data Infrastructure, Python (Programming Language), Machine Learning, Server Administration, Software Engineering, Systems Integration, Cloud Platform System, Deployment Automation, Machine Learning Operations, Docker
- **Published:** October 21, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-machine-learning-infrastructure-san-francisco-ca--47999c67-4255-4d23-932a-5b7c85abb4a0

## About the Role

Software Engineering, Machine Learning, Mineral/Metal Mining, Artificial Intelligence (AI), Real Estate, Manufacturing, Hardware-Software Integration, Machine Learning, Data Science, Software Administration, * 3+ years of experience as a software engineer, and the ability to work independently in a fast-paced, dynamic environment, taking initiative to drive results.

 * Background in data infrastructure, platform infrastructure or ML infrastructure is preferred.
 * Enthusiastic about learning new codebases, and making changes in large complex systems safely and efficiently.
 * Proficiency in Python and Java programming languages. We're looking for engineers who are comfortable with multiple languages and have experience building scalable high-performance systems
 * Familiarity with Azure and GCP cloud environments, and deployment tools such as Docker and Kubernetes
 * Experience in real estate, food technology, or related industries is a bonus, though not required. A passion for solving complex problems in these sectors is key.

## Description

This role is based in our San Francisco office location. As a company driven by innovation and continuous change, close collaboration is essential. We're constantly reimagining our industry, creating new products, and refining our processes, and we do our best work together. That's why all of our office-based teams work onsite, five days a week.