Responsibilities
1. Responsible for the algorithm work of multi-modality, computer vision, and natural language processing in the international live broadcast business, explore the application of content understanding technology in various businesses, and achieve the growth of business indicators and technological innovation 2. Responsible for training and optimization of large models related to live broadcast: including training optimization of multi-modal large models for painting style business, producing high-quality data and improving model governance capabilities through Post-training, as well as training optimization of multi-modal base large models and full-modal large models, and realizing SOTA capabilities on open source and live broadcast business data sets through the production and collection of high-quality data for live broadcast scenarios 3. Continuously iterate the ability to understand and represent live broadcast content at different granularities, improve the technical depth and application of live multi-modal recommendations, and quickly manage live broadcast review scenarios 4. Responsible for the construction and optimization of the full machine review architecture of review scenarios, and explore innovative applications of large models in review business 5. Research efficient and real-time long video understanding technical capabilities to support different business needs of live broadcast scenarios (content diversity recognition, interactive understanding, professional modeling, etc.).
Qualifications
1. Master the technology and application experience related to multi-modal understanding, be familiar with the structure and training framework of mainstream large models, and continue to track the latest progress in related fields 2. Have business passion, strong sense of responsibility, be proactive, and have good communication and teamwork skills 3. Have imagination and independent thinking ability, have excellent experimental analysis and problem-solving skills, be able to propose ideas and put them into practice for verification 4. Applicants who have led projects or published high-level papers in the direction of multi-modality or large models are preferred 5. Applicants with end-to-end experience in content understanding in recommended search or review scenarios are preferred.
