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# Software Engineer Graduate (MLOps) - 2027 Start

- **Company:** Tiktok Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Starter
- **Salary:** $128,000.0 - $256,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), C++ (Programming Language), Computer Programming, Linux, Monitoring of Systems, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Software Deployment, Information Technology, Machine Learning Operations
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=44198298dd60d408

## 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., Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment

## 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.


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