Research Scientist, ML Recommendation Systems, Applied Machine Learning Team

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 such as TikTok, Douyin. 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

You Will: 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:
• A Bachelor's degree in Computer Science, Computer Engineering, or a related technical field is required. A Ph.D. in a relevant field is highly preferred.
• 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.
• A track record of publications at accredited peer-reviewed conferences such as NeurIPS, ICML, ICLR, KDD, RecSys, WWW