Research Scientist Intern– E-commerce Recommendation(LLM Applications) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)
Core
Build a foundational large multimodal model and pluggable Agent framework for end-to-end intelligent decision-making in global e-commerce scenarios, including demand forecasting, traffic allocation, and personalized recommendations.
Role type
PhD Research Scientist (LLM Applications & Recommendation Systems)
Builds
A unified multimodal foundation model integrating users, products, content, logistics, and inventory; modular Agent frameworks for task planning and multi-turn interaction.
Domain
E-commerce, Large Language Models (LLMs), Multimodal AI, Recommendation Systems
Deliverable
production ML models
Required skills
Machine learning, Large Language Models (LLMs), Multimodal learning, Big data frameworks (Hadoop, MapReduce, Spark), Deep learning frameworks (TensorFlow, PyTorch), Distributed training, Model training and deployment
Preferred skills
Model compression and inference acceleration (quantization, pruning, distillation, TensorRT), Computer Vision & Multimodality (image/video classification, segmentation, object detection, OCR, graph neural networks), Natural Language Processing (pretraining, NLU, NLG, transfer learning), Academic publications in top-tier conferences (CVPR, ICCV, ACL, EMNLP)
Technologies
Hadoop, MapReduce, Spark, TensorFlow, PyTorch, TensorRT, Diffusion Models, RLVR
Responsibilities
Build industry-leading recommendation systems and explore generative recommendation techniques; Develop multi-model and cross-scenario systems for unified recommendation across livestreams, short videos, and search; Deliver end-to-end machine learning solutions to address critical product challenges; Optimize algorithms and infrastructure to improve recommendation performance; Work with cross-functional teams to design product strategies and build solutions for market growth.
Seniority
PhD Intern (Research)
