MLOps Engineer Intern: Scale Production ML Pipelines

TikTok; Singapore; On-site; SGD 10,000 - 17,000; Full time

Job summary

TikTok is seeking interns for its Global SRE team to support the reliability of the machine learning infrastructure powering ByteDance’s global advertising systems. You will help ensure the stability, scalability, and efficiency of the ML lifecycle, including data pipelines, model development, training, deployment, inference, and monitoring.

This internship offers hands-on experience, industry exposure, and opportunities to apply knowledge to real-world challenges, while building a strong

Qualifications

• Currently pursuing an Undergraduate/Master’s in Computer Science or Software Engineering or related discipline.
• Expertise in Linux operating systems, networking, storage.
• Experience programming in Python, Go, C, C++, or Java.
• Experience in troubleshooting application issues or production operations.
• Effective communication skills and a sense of ownership and drive.

Responsibilities

• Define and drive SLOs for online ML inference systems ensuring reliability, availability, and performance.
• Ensure reliability and operational excellence of offline ML training pipelines; improve training job success rates.
• Drive infrastructure capacity planning and resource management for ML workloads; optimize GPU/CPU utilization.
• Lead enablement of new ML frameworks and GPU platforms for large-scale production deployment; maintain model quality and business performance.
• Design, build, and maintain MLOps platforms and automation tools (quota management, diagnostics, monitoring, resource management).

Skills

Python

Go

C

C++

Java

Linux

Networking

Storage

Education

Undergraduate/Master’s in Computer Science or Software Engineering

Job description

TikTok is seeking interns for its Global SRE team to support the reliability of the machine learning infrastructure powering ByteDance’s global advertising systems. You will help ensure the stability, scalability, and efficiency of the ML lifecycle, including data pipelines, model development, training, deployment, inference, and monitoring.

This internship offers hands-on experience, industry exposure, and opportunities to apply knowledge to real-world challenges, while building a strong