Job Description

Team Introduction
The Applied Machine Learning (AML) team combines system engineering and the art of machine learning to develop and run massively distributed recommendation system around the world. In the team, you'll have the opportunity to sharpen your expertise in coding, performance analysis and large system operation, and get heavily involved in the process of hardware/capacity decision-making. You ensure that the very centric machine learning services at ByteDance have the highest level of availability, as well as creating highly automated systems and pipelines.

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.

About The Team
The Applied Machine Learning (AML) team combines system engineering and the art of machine learning to develop and run massively distributed recommendation system around the world.

In the team, you'll have the opportunity to sharpen your expertise in coding, performance analysis and large system operation, and get heavily involved in the process of capacity decision-making. You ensure that the very centric machine learning services at Bytedance have the highest level of availability, as well as creating highly automated systems and pipelines.

Responsibilities
- Responsible for building and evolving Lagrange - the central console platform that supportsAl-related engineering workflows across the organization
- Analyse user needs and develop software solutions, applying principles and techniques of computer science, engineering, and mathematical analysis.
- Work on problems with real technical depth and long-term impact: platform architecture, engineering productivity, system scalability, operational intelligence, and the practical integration of Al into engineering and operations workflows.
- Create the infrastructure and platform capabilities that make large-scale Al development more efficient, reliable, and intelligent