Site Reliability Engineer (Machine Learning Systems), TikTok Infrastructure

TikTok will be prioritising applicants who have a current right to work in Singapore, and do not require TikTok's sponsorship of a visa.

TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. TikTok has global offices including Los Angeles, New York, London, Paris, Berlin, Dubai, Singapore, Jakarta, Seoul and Tokyo.

Why Join Us
Creation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible.
Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day.
To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
At TikTok, we create together and grow together. That's how we drive impact - for ourselves, our company, and the communities we serve.
Join us.

About the Team
The Machine Learning (ML) System team combines system engineering and the art of machine learning to develop and maintain massively distributed ML training and Inference system/services around the world.

In our team, you'll have the opportunity to build the large scale heterogeneous system integrating with GPU/RDMA/Storage and keep it running stable and reliable, enrich your expertise in coding, performance analysis and distributed system, and be involved in the decision-making process. You'll also be part of a global team with members from United States, China and Singapore working collaboratively towards unified project direction.

Responsibilities
1. Responsible for ensuring our internal systems are operating efficiently for model development, training and deployment;
2. Responsible for resource management and planning, cost and budget, including computing and storage resources;
3. Responsible for global system disaster recovery, cluster machine governance, stability of business services, resource utilisation improvement and operation efficiency improvement;
4. Build software products and systems to monitor and manage the ML infrastructure and services;
5. Be part of the global team roster that ensures system and business on-call support;
6. Research, design, and develop computer and network software or specialised utility programs;
7. Analyse user needs and develop software solutions, applying principles and techniques of computer science, engineering, and mathematical analysis;
8. Update software, enhances existing software capabilities, and develops and direct software testing and validation procedures;
9. Work with computer hardware engineers to integrate hardware and software systems and develop specifications and performance requirements;