PhD Intern, Recommendation Systems – Recommendation Systems

Job Description

Research Scientist Intern– E-commerce Recommendation(LLM Applications) – Global Frontier Tech Recruitment Program – 2027 Start (PhD) | TikTok

The Tone:
This is an internship at TikTok, primarily focused on e-commerce recommendations and LLM applications, with pay details linked to Los Angeles County. TikTok builds the leading destination for short-form mobile video and operates a global e-commerce business known as TikTok Shop. This role matters because it provides PhD students with an opportunity to contribute directly to product innovation and research, supporting the organization’s future plans and emerging technologies in e-commerce. The work aims to establish a foundational large model for Global E-commerce, driving business growth and user engagement.

The TL;DR
• Role: Internship
• Location: In-person, Los Angeles County
• Pay: $60 hourly
• Team: The Data–E-commerce team serves as the core algorithm and technical backbone of the Global E-commerce business.
• Mission: Build a foundational large model tailored for Global E-commerce scenarios, unifying key elements and enabling end-to-end intelligent decision-making.
• Tech Stack: Hadoop, MapReduce, Spark, TensorFlow, PyTorch

What You’ll Actually Do
• Recommendation Systems: Build industry-leading recommendation systems to improve user experience, content ecosystem, and platform security.
• Generative Techniques: Explore generative recommendation techniques, including Diffusion Models and prompt learning, to create new capabilities in content discovery.
• Unified Recommendations: Build multi-model and cross-scenario systems that enable unified recommendation across livestreams, short videos, and search functionalities.
• Machine Learning Solutions: Deliver end-to-end machine learning solutions to address critical product challenges within the e-commerce domain.
• System Ownership: Own the full-stack machine learning system, optimizing algorithms and infrastructure to enhance recommendation performance.

The Must-Haves
• Background: Doctorate Degree student pursuing Computer Science, Computer Engineering, or a related technical discipline. Possess a strong foundation in machine learning and knowledge of cutting-edge AI technologies.
• Experience: Familiarity with big data frameworks such as Hadoop, MapReduce, and Spark. Experience with TensorFlow or PyTorch for model training and deployment, including an understanding of training acceleration techniques like mixed precision and distributed training. Publications in top-tier academic conferences or competition experience are preferred.
• Skills: Machine learning, deep learning frameworks (TensorFlow, PyTorch), big data processing, model training and deployment, training acceleration.
• Bonus: Knowledge of model compression and inference acceleration techniques such as quantization, pruning, distillation, and TensorRT optimization. Expertise in Computer Vision & Multimodality or Natural Language Processing (NLP) with research experience in areas like large-scale CV/multimodal models, LLMs, image/video classification, multilingual learning, and relevant competition achievements or accredited conference publications.