Research Scientist - Recommendation Modeling scale Computing Infrastructure - Global Frontier Tech

Position: Research Scientist - Recommendation Modeling & Large-scale Computing Infrastructure - Global Frontier Tech Recruitment
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 our Company. 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.

Team Introduction:
Data AML is Byte Dance's centralized Machine Learning Platform, providing training and inference systems for recommendation, advertising, search, live-streaming, e-commerce for businesses such as Tik Tok ecosystem. It provides powerful Machine Learning computing power to internal business units within the company and conducts research on some general and innovative algorithms for issues in these businesses. At the same time, it also provides some core capabilities of Machine Learning and Recommender systems to external enterprise customers through Volcano Engine.

In addition, AML also conducts some cutting-edge research in fields such as Al for Science and scientific computing. Topic Content:
Large-scale recommendation systems are being increasingly adopted across products such as short-video, text-based multimodal, and image platforms, with modality-specific in tremendous laying an ever-growing role in recommendations. Leveraging our latest research breakthroughs and broad industry insights, we believe modality information serves effectively as generalizable features to support recommendation and other business scenarios. Research on ultra-large-scale multimodal recommendation systems holds significant potential.

Minimum Qualification(s):

- Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.

- Research background in ML/AI, with a focus on inference optimization, model acceleration, or efficient ML systems.

- Experience in programming, included but not limited to, the following programming languages: C, C++, Java or Python.

- Problem-solving skills, self-driven learning mindset, and effective communication. Preferred Qualification(s):

- Demonstrated ability to translate research into practical system implementations.

- Experience (academic or practical) in areas such as advertising or recommendation systems, search, distributed systems, or big data processing.