Singapore, SingaporeFull TimeEntry-levelPosted Today
Team Introduction
Data AML is ByteDance's Machine Learning mid-platform, providing training and inference systems for recommendation/advertising for businesses such as Douyin, Jinri Toutiao, and Xigua Video. It provides powerful Machine Learning computing power for internal business units within the company and conducts research on some general and innovative algorithms for issues in these businesses.
We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at ByteDance.
Successful candidates must be able to commit to an onboarding date by end of year 2027. 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 ByteDance and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
Responsibilities
- Responsible for the overall architecture design and implementation of model inference services, building a high-performance, highly available, and scalable enterprise-level inference system for large-parameter, high-complexity AI models, overcoming various architectural challenges in the implementation of complex model inference, and supporting the efficient launch of models across all business scenarios.
- Responsible for the R&D and optimization of the core modules of the inference framework, covering core capabilities such as inference engine scheduling, monitoring and alerting, canary release, etc., continuously iterating on the framework performance, and resolving performance bottlenecks, resource bottlenecks, and stability issues in high-concurrency and large-model inference scenarios.
- Keep track of the latest inference technologies in the industry, conduct technology selection and innovation in combination with business scenarios, accumulate distributed high-concurrency service architecture solutions, and promote the upgrade and standardization of the team's technical system.
Data AML is ByteDance's Machine Learning mid-platform, providing training and inference systems for recommendation/advertising for businesses such as Douyin, Jinri Toutiao, and Xigua Video. It provides powerful Machine Learning computing power for internal business units within the company and conducts research on some general and innovative algorithms for issues in these businesses.
We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at ByteDance.
Successful candidates must be able to commit to an onboarding date by end of year 2027. 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 ByteDance and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
Responsibilities
- Responsible for the overall architecture design and implementation of model inference services, building a high-performance, highly available, and scalable enterprise-level inference system for large-parameter, high-complexity AI models, overcoming various architectural challenges in the implementation of complex model inference, and supporting the efficient launch of models across all business scenarios.
- Responsible for the R&D and optimization of the core modules of the inference framework, covering core capabilities such as inference engine scheduling, monitoring and alerting, canary release, etc., continuously iterating on the framework performance, and resolving performance bottlenecks, resource bottlenecks, and stability issues in high-concurrency and large-model inference scenarios.
- Keep track of the latest inference technologies in the industry, conduct technology selection and innovation in combination with business scenarios, accumulate distributed high-concurrency service architecture solutions, and promote the upgrade and standardization of the team's technical system.