Gen AI Recommender Intern for E‑Commerce

TikTok; San Jose (CA); On-site; USD 68,000 - 97,000; Part time

Benefits offered by this job

Health insurance

Wellbeing benefits

Housing allowance

10 paid holidays

Paid sick time

Job summary

TikTok is offering a PhD internship for the E-commerce Recommendation Team in San Jose. You will conduct cutting-edge research in generative recommender systems, LLM-based enhancements, and agentic architectures to improve personalization and efficiency on TikTok's shopping experiences.

You will collaborate with industry experts, publish insights, and contribute to next-generation algorithms aimed at scalable, long-term value.

Qualifications

• Currently pursuing a PhD in Computer Science, Electrical Engineering, Mathematics, Statistics or related field.
• Strong foundation in ML/DL with research or project experience in LLMs, RL, generative models, or recommender systems.
• Proficient in Python and at least one DL framework (PyTorch, TensorFlow, or JAX).

Responsibilities

• Generative Recommendation Research: advance recommender systems with generative methods and optimize training/inference on GPUs.
• LLM for Recommendation Research: use LLMs/RL to improve semantic understanding and reasoning in recommendations.
• Agentic Recommendation Research: explore self-evolving agents for real-time personalized decisions.
• Long-Term Value and User Experience Modeling: develop algorithms to measure and optimize LTV and user experience.

Skills

Python programming

Machine learning

Deep learning

PyTorch

TensorFlow

JAX

Education

PhD student in CS/EE/Math/Stats

Tools

PyTorch

TensorFlow

JAX

Job description

TikTok is offering a PhD internship for the E-commerce Recommendation Team in San Jose. You will conduct cutting-edge research in generative recommender systems, LLM-based enhancements, and agentic architectures to improve personalization and efficiency on TikTok's shopping experiences.

You will collaborate with industry experts, publish insights, and contribute to next-generation algorithms aimed at scalable, long-term value.