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# Machine Learning MLOps Engineer Graduate (MLOps)

- **Company:** Tiktok Inc
- **Location:** San Jose, CA, United States
- **Experience:** Starter
- **Salary:** $128,000.0 - $256,000.0
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), C++ (Programming Language), Computer Programming, Linux, Fault Tolerance, Monitoring of Systems, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Software Deployment, Information Technology, Machine Learning Operations
- **Published:** October 14, 2026
- **Apply:** https://www.themuse.com/jobs/tiktok/machine-learning-mlops-engineer-graduate-mlops-2027-start

## About the Role

Individuals who are completing or have recently completed a Bachelor's/ Master's degree in Computer Science or a related discipline.

 - Expertise in Linux operating systems, networking, storage.
 - Experience programming in at least one of the following programming languages: Python, Go, C, C++, or Java.
 - Experience in troubleshooting application issues, or production operations.
 - Effective communication skills and a sense of ownership and drive.

Preferred Qualifications:

 - Experience in SRE of machine learning systems.
 - Experience in SRE of ads/recommendation/search systems.

## Description

The monetization technology team works on building and running large-scale, globally distributed, fault-tolerant ads systems. SREs keep the systems up and running with the highest level of availability, ensuring our users have the best experience possible., Define and drive Service Level Objectives (SLOs) for online machine learning inference systems, ensuring the reliability, availability, and performance of large-scale production inference services.

 - Ensure the reliability and operational excellence of offline machine learning training pipelines, continuously improving training job success rates
 - Drive infrastructure capacity planning and resource management for machine learning workloads, ensuring compute resources meet evolving business demands while continuously improving GPU and CPU utilization through performance optimization.
 - Lead the enablement of new machine learning frameworks and GPU platforms, driving large-scale production deployment while maintaining model quality and business performance.
 - Design, build, and maintain MLOps platforms and automation tools, including quota management, job diagnostics, monitoring and observability, resource management, and operational tooling.