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# MTS - Infrastructure

- **Company:** Omnifold, Inc.
- **Location:** San Francisco, CA, United States
- **Salary:** $150,000 - $300,000
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Cloud Computing, Cyber Security, Databases, Continuous Integration, Extract Transform Load (ETL), Python (Programming Language), Data Logging, Graphics Processing Unit (GPU), Software Security, Information Technology, Machine Learning Operations, Software Version Control
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/mts-infrastructure-san-francisco-ca--7f3cf43f-566e-4ac0-94a6-9ffa9c742cb7

## About the Role

Artificial Intelligence (AI), Forecasting, Process Modeling, Security Infrastructure, Resource Management, Cloud Computing, Cost Control, GPU (Graphics Processing Unit), Database Extract Transform and Load (ETL), Customer/Client Research, * Experience with cloud computing (especially GPU workloads), CI/CD infrastructure-as-code. We run on AWS

 * Familiarity with or interest in ML workflows
 * Security fundamentals: encryption, access controls, compliance basics
 * Python proficiency
 * Must have a strong Computer Science background

## Description

* We train a unique model for each customer, which means model training and inference work differently here than at any other company. You'll never get more reps building model training infrastructure!
 * Our team has very fast iteration speed but needs robust monitoring to pick up signal on user patterns. This is especially important as our application interface for AI-driven forecasting is unique on the market.

What you'll own

 * Deployment: Reliable processes for getting models and services into production
 * Security: Data isolation between customers, product security, infrastructure hardening (SOC2 compliance and beyond)
 * Cloud resource management: GPU allocation, instance sizing, cost optimization
 * Monitoring and logging: Visibility into what's running, what's failing, and why
 * Data and ML ops: ETL pipelines from varied customer data sources, model versioning and lifecycle management
 * Automated testing: Building the test infrastructure that lets us ship with confidence