Machine Learning Ops Engineer Graduate (Ads Infra) - 2027 Start

Responsibilities

Team Introduction
MLOps - Global SRE team is responsible for the reliability of the machine learning infrastructure powering ByteDance's global advertising systems. We ensure the stability, scalability, and operational efficiency of the entire machine learning lifecycle, including data pipelines, model development, training, deployment, inference, monitoring, and continuous optimization.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.
Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities
- 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.

Qualifications

Minimum Qualifications
- Individuals who are completing or have recently completed a Bachelor's/ Master's degree in Computer Science or Software Engineering 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.

Responsibilities

Responsibilities

Team Introduction
MLOps - Global SRE team is responsible for the reliability of the machine learning infrastructure powering ByteDance's global advertising systems. We ensure the stability, scalability, and operational efficiency of the entire machine learning lifecycle, including data pipelines, model development, training, deployment, inference, monitoring, and continuous optimization.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.
Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities
- 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.

Qualifications

Qualifications

Minimum Qualifications
- Individuals who are completing or have recently completed a Bachelor's/ Master's degree in Computer Science or Software Engineering 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.