Research Scientist Graduate (ML Recommendation Systems) - 2026 Start (PhD)

You will be joining our Applied Machine Learning team, a central team responsible for delivering state-of-the-art solutions powering our company's recommendations, ads, and search systems across various products. We own the end-to-end ML lifecycle, from ideation and research to building, deploying, and iterating on models in production. We are looking for candidates who are passionate about solving complex problems and have a strong foundation in machine learning theory and practice.

Some of the projects we have been working on:

• Large Scale Recommendation Models
• End-to-End Generative Recommendation Systems
• Reinforcement Learning for User Personalization in Recommendation Systems

We are looking for talented individuals to join our team in 2026. 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 2026. Please state your availability and graduation date clearly in your resume.

Responsibilities: In this role, you will drive the next wave of innovation for our recommendation systems, directly shaping the user experience by:

• Build and scale up machine learning models for recommendation systems
• Research and apply multi-modal techniques (leveraging text, image, video) to create a holistic understanding of content and user preferences
• Pioneer new modeling strategies by researching and integrating long-term user behavior signals to drive sustained engagement and satisfaction, by using techniques such as reinforcement learning
• Partner closely with the infrastructure team to co-design and optimize next-generation recommendation model architectures and systems, ensuring high-performance, low-latency, and cost-efficient training and inference at a massive scale.
• Work hand-in-hand with product, engineering, and design teams to rigorously test and deploy end-to-end solutions, validating their impact and ensuring they create a seamless and enhanced user experience.Minimum Qualifications:
• Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• At least 5 years of experience in proficiency in one or more programming languages such as Python or C++, and deep learning frameworks like PyTorch or TensorFlow.
• Demonstrated expertise in designing, building, and scaling machine learning models for recommendation systems.
• Deep understanding and hands-on experience with modern deep learning techniques, including Transformers, Large Language Models (LLMs), and multi-modal learning.
• Proven experience in building and deploying end-to-end ML pipelines in a production environment.