Production Engineer Intern (AML Serving) - 2027 Start

Team Introduction
The production engineering team for the Applied Machine Learning (AML) organization is at the heart of what we do. We are a global team of engineers who blend deep systems knowledge with software engineering to build and run the large-scale, distributed systems that power our machine learning products. Our mission is to ensure that ByteDance's core machine learning services are reliable, scalable, and efficient.
As a production engineer intern, you will be immersed in a fast-paced, high-impact environment. You will have the opportunity to work on real-world challenges, receive mentorship from experienced engineers, and contribute to systems that serve millions of users worldwide. This is a unique chance to develop your skills in coding, performance analysis, and large-scale system operations.

We are looking for talented individuals to join our team in 2027. As an intern, 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
- Assisting in the design and development of software and tools to enhance system automation, monitoring, and operational efficiency.
- Participating in troubleshooting and resolving system issues, analyzing root causes, and implementing preventative measures.
- Contributing to the enhancement of existing software by updating capabilities and supporting testing and validation procedures.
- Collaborating with software and hardware engineers to understand system requirements and contribute to performance improvements.
- Learning and applying principles of computer science, engineering, and mathematical analysis to solve real-world problems.