Machine Learning MLOps Engineer Graduate (MLOps)
TikTok
About the Role
- This role is for a graduate starting in 2027.
- Successful candidates must commit to an onboarding date by the end of the year.
- Candidates can apply to a maximum of two positions globally.
- Applications will be reviewed on a rolling basis.
Key Responsibilities
- Define and drive Service Level Objectives (SLOs) for online machine learning inference systems, ensuring 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.
Requirements & Tech Stack
- Expertise in Linux operating systems, networking, storage.
- Programming experience in at least one of the following 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.
Qualifications
- Individuals who are completing or have recently completed a Bachelor’s/Master’s degree in Computer Science or a related discipline.
- Preferred: Experience in SRE of machine learning systems.
- Preferred: Experience in SRE of ads/recommendation/search systems.