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 computational performance optimization of ByteDance's recommendation mid-platform models, conduct in-depth tuning for inference/training bottlenecks in business scenarios, and improve computing utilization.
- Lead the design and development of high-performance kernel libraries, covering general-purpose and business-customized kernels, including general computation and communication parallelism, to ensure the ultimate performance of kernels.
- Deeply cultivate model compilation optimization technology, and based on directions such as graph optimization, kernel fusion, computation scheduling, and code generation, build and improve the mid-platform model compilation system.
- Collaborate with the business and algorithm teams to identify performance issues, provide full stack performance analysis, bottleneck diagnosis, and optimization solutions, consolidate general-purpose performance optimization components, toolchains, and platform capabilities, and empower multiple internal business.
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 computational performance optimization of ByteDance's recommendation mid-platform models, conduct in-depth tuning for inference/training bottlenecks in business scenarios, and improve computing utilization.
- Lead the design and development of high-performance kernel libraries, covering general-purpose and business-customized kernels, including general computation and communication parallelism, to ensure the ultimate performance of kernels.
- Deeply cultivate model compilation optimization technology, and based on directions such as graph optimization, kernel fusion, computation scheduling, and code generation, build and improve the mid-platform model compilation system.
- Collaborate with the business and algorithm teams to identify performance issues, provide full stack performance analysis, bottleneck diagnosis, and optimization solutions, consolidate general-purpose performance optimization components, toolchains, and platform capabilities, and empower multiple internal business.