Research Scientist Intern- E-commerce Recommendation- LLM Applications - Global Frontier Tech R[...]

We are looking for talented individuals to join us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands‑on learning, enriching community‑building and development events, and collaboration with industry experts. Team Introduction Global E-commerce is an e-commerce business built on TikTok (also known as TikTok Shop). It aims to become the preferred platform where users discover and purchase high-quality products at competitive prices. Across multiple scenarios - including live-streaming e-commerce, video-based commerce, and marketplace (shelf-based) commerce, the team is committed to delivering a more personalized, proactive, and efficient shopping experience for users, while providing merchants with a stable and reliable platform. Its mission is to bring unique and high-quality products to global markets and make a better lifestyle easily accessible. The Data-E-commerce team serves as the core algorithm and technical backbone of the Global E-commerce business. It focuses on algorithmic innovation in the e-commerce domain, helping users efficiently discover products of interest, ensuring transaction safety, and improving intelligence across all stages of the transaction process. Here, you will collaborate with top‑tier product and engineering teams to tackle both technical and business challenges, driving the deep integration of advanced technologies into real‑world e-commerce scenarios. Project Overview The Global E-commerce ecosystem has accumulated massive heterogeneous data, including user behavior, product images and text, multimedia content, sales data, and logistics time series. However, traditional models still face significant limitations in long‑term forecasting, cross‑modal understanding, and complex decision‑making. This project aims to build a foundational large model tailored for Global E-commerce scenarios. It will unify key elements such as users, products, content, logistics, and inventory into a single modeling framework. On top of this, a modular, pluggable Agent framework will be designed to integrate capabilities such as task planning, tool usage, multi‑turn interaction, and environmental awareness. This enables end‑to‑end intelligent decision‑making across workflows like demand forecasting, traffic allocation, and personalized recommendation. Key Challenges Heterogeneous Data Fusion & Alignment: Unify user behavior, sales time series, and multimodal content into aligned high-dimensional representations. Collaboration Between Recommendation LLMs and World Models: Leverage LLMs and world models to generate personalized recommendations end‑to‑end. Item Tokenization for Recommendation: Encode hundreds of millions of items and massive user behavior data for large­scale training, optimizing via advanced architectures, post‑training (e.g., RLVR), and efficient inference. Multimodal Large Models for E‑commerce: Build multilingual, multimodal models achieving SOTA performance and powering intelligent e‑commerce agents. Agent Evaluation, Safety & Compliance: Establish robust metrics and safeguards to ensure performance, safety, and compliance in real‑world scenarios. Project Value Technical Value - Build a general‑purpose multimodal foundation model, leveraging iterative improvements in models, data, and compute to achieve scaling‑law-driven growth and establish a strong technical foundation. Business Value - Establish a foundational large model for Global E‑commerce, leveraging generative recommendation, temporal models, and agent‑based systems to drive GMV growth and user retention, forming a high‑leverage revenue engine. Responsibilities Build industry‑leading recommendation system, improving user experience, content ecosystem and platform security; Explore generative recommendation techniques, including Diffusion Models, prompt learning, and multimodal content generation, to unlock new capabilities in content discovery. Build multi‑model and cross‑scenario systems enabling unified recommendation across livestreams, short videos, and search. Deliver end‑to‑end machine learning solution to address critical product challenges; Own the full stack machine learning system and optimize algorithms and infrastructure to improve recommendation performance. Work with cross functional teams to design product strategies and build solutions to grow TikTok in important markets. Minimum Qualifications Currently pursuing PhD in Computer Science, Computer Engineering, or a related technical discipline. Strong foundation in machine learning, with knowledge of cutting‑edge AI technologies; publications in top‑tier academic conferences or competition experience are preferred. Familiarity with big data frameworks such as Hadoop, MapReduce, and Spark. Experience with TensorFlow or PyTorch for model training and deployment; understanding of training acceleration techniques such as mixed precision and distributed training. Preferred Qualifications Knowledge of model compression and inference acceleration techniques, including but not limited to quantization, pruning, distillation, and TensorRT optimization. Expertise in at least one of the following areas: Computer Vision & Multimodality: In‑depth research experience in multimedia or computer vision fields, including but not limited to image search, image/video classification and recognition, image segmentation, object detection, OCR, graph neural networks, multimodal learning, and unsupervised/self‑supervised learning. Experience with large‐scale CV/multimodal models, particularly in e‑commerce scenarios, including developing and optimizing multimodal models for e‑commerce videos and products. Ability to integrate LLMs with video/product representations to support tasks such as multimodal classification, video QA, cross‑modal retrieval, and product categorization, with performance significantly surpassing production models. Strong hands‑on experience, with achievements in competitions such as Kaggle, COCO, ImageNet, ActivityNet, or ICPC. Familiarity with state‑of‑the‑art research, with publications in accredited conferences such as CVPR, ICCV, or ECCV. NLP: In‑depth research experience in NLP, including but not limited to pretraining techniques, natural language understanding, multilingual and cross‑lingual learning, natural language generation, transfer learning, and semi‑supervised learning. Experience with large language models (LLMs), including developing NLP models to unify tasks in e‑commerce scenarios and applying them in real‑world business contexts. Strong practical experience, with achievements in competitions such as Kaggle, GLUE, SuperGLUE, or CLUE. Familiarity with state‑of‑the‑art research, with publications in accredited conferences such as ACL or EMNLP. #J-18808-Ljbffr SpeedyApply LLC