Research Scientist Intern– E-commerce Recommendation; LLM - Global Frontier Tech R

Responsibilities

We are looking for talented individuals to join us for a PhD internship in 2027. The internship will allow students to actively contribute to our products and research, and help shape the organization’s future plans and emerging technologies.

Core responsibilities include:

• Build industry-leading recommendation systems to improve user experience, content ecosystem, and platform security.
• Explore generative recommendation techniques such as Diffusion Models, prompt learning, and multimodal content generation to unlock new capabilities in content discovery.
• Build multi-model and cross-scenario systems that enable unified recommendation across live streams, short videos, and search.
• Deliver end-to-end machine learning solutions to address critical product challenges.
• Own the full stack machine learning system, optimizing algorithms and infrastructure to improve recommendation performance.
• Collaborate with cross‑functional teams to design product strategies and build solutions that grow the platform in important markets.

Global E‑commerce is an e‑commerce business built on Tik Tok Shop. The Data‑E-commerce team serves as the core algorithm and technical backbone of our business, focusing on algorithmic innovation in the e‑commerce domain. You will collaborate with product and engineering leaders to tackle both technical and business challenges, driving the deep integration of advanced technologies into real‑world e‑commerce scenarios.

We will build a foundational large model tailored for Global E‑commerce scenarios. The model will unify key elements such as users, products, content, logistics, and inventory into a single modeling framework. In addition, we will design an Agent framework to integrate capabilities such as task planning, tool usage, multi‑turn interaction, and environmental awareness, enabling end‑to‑end intelligent decision‑making across workflows like demand forecasting, traffic allocation, and personalized recommendation.

• Unify heterogeneous data (behavior, sales time series, multimodal content) into aligned high‑dimensional representations.
• Collaborate recommendation 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 large‑scale training.
• Build multilingual, multimodal large models achieving state‑of‑the‑art performance for e‑commerce.
• Establish robust metrics and safeguards for agent evaluation, safety, and compliance.

• Technical:
  Build a general‑purpose multimodal foundation model that drives scaling‑law‑generated growth and establishes a strong technical foundation.
• Business:
  Establish a foundational large model that powers generative recommendation, temporal models, and agent‑based systems to drive global revenue and user retention.

Minimum qualifications:

• Currently pursuing a 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, Map Reduce, and Spark.
• Experience with Tensor Flow 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 quantization, pruning, distillation, and Tensor
  
  RT optimization.
• Expertise in at least one of the following areas:
• Computer Vision & Multimodality: in‑depth research experience in multimedia or computer vision fields, large‑scale multimodal model development for e‑commerce scenarios.
• Natural Language Processing (NLP): in‑depth research experience in NLP, large language models, and enterprise‑level applications for e‑commerce.

Compensation: $57 per hour (Hourly).

Benefits:
Health insurance, life insurance, wellbeing benefits, 10 paid holidays, and paid sick time (56 hours if hired in the first…