We are building an AI-first quantitative trading fund that leverages cutting-edge machine learning, agentic AI frameworks, and high-performance infrastructure to discover and scale alpha across global markets. Our systems integrate multi-agent architectures, real-time factor generation, and automated strategy pipelines to stay ahead in fast-evolving trading environments.
Develop and deploy data‑driven solutions using manufacturing data (e.g., machine data, quality data, MES/SCADA data) for manufacturing performance improvement
Participate in building large-scale (billion level) E-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc.
Exploring Cutting-Edge NLP Technologies: From basic tasks like word segmentation and Named Entity Recognition (NER) to advanced business functions like text and multimodal pre-training, query analysis, and fundamental relevance modeling, we apply deep learning models throughout the pipeline where every detail presents a challenge.
Participate in building large-scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc.
Design, build, and maintain MLOps pipelines for training, testing, deploying, and monitoring AI/ML models in production, ensuring stable, reliable, and scalable performance.
Search LLM Foundation R&D: Build and optimize the search-domain LLM foundation, integrating search knowledge to rapidly deliver business value; develop and refine the post-training pipeline for search LLMs (ultra-long-text / colloquial-text pre-training, image-text / video multimodal representation, e-commerce product multimodal representation learning, etc.); participate in LLM inference optimization (long-context optimization, model efficiency optimization).
Design, develop, and evaluate Agentic AI and multi-agent architectures for task decomposition, planning, reasoning, coordination, and workflow orchestration within the Digital ATCO Assistant project.
Responsible for using algorithms to mine, identify, and predict risks and low-quality merchants, products, and creators in e-commerce scenarios, and assist businesses in managing and controlling them.
Participate in building large-scale (billion level) E-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc.
Design, develop, and evaluate Agentic AI and multi-agent architectures for task decomposition, planning, reasoning, coordination, and workflow orchestration within the Digital ATCO Assistant project.