ML Engineer — Generative Recommendation & Multimodal LLMs

TikTok; San Jose (CA); On-site; USD 128,000 - 317,000; Full time

Job summary

TikTok is seeking graduates to join the Recommendation Foundation team within Data – Global E-commerce. You will help build event-sequence driven generative recommendation by integrating LLMs, VLMs, and multimodal understanding to push beyond click prediction.

You will participate in training across pre-, mid-, and post-training, design tokenizers, and contribute to end-to-end systems. The role offers bold ideas, growth, and a path to impactful research within a fast-moving, global tech company.

Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics or related field.
• Strong foundation in machine learning and deep learning, with strong interest in LLMs and generative recommendation.
• Proficiency in Python and experience with deep learning frameworks such as PyTorch.

Responsibilities

• Participate in the full training lifecycle of Recommendation Foundation Models, including pre-training, mid-training, and post-training.
• Design and train multimodal semantic tokenizers for recommendation items, leveraging multimodal foundation models to encode rich item content into discrete semantic tokens and raise the performance ceiling of Recommendation Foundation Models.
• Develop LLM-native recommendation by incorporating recommendation tasks directly into large language model training and leveraging world knowledge to improve recommendation quality.
• Build the next generation of recommendation systems powered by Recommendation Foundation Models, spanning retrieval, ranking, and end-to-end generative recommendation.

Skills

Machine learning

Deep learning

Research mindset

Education

Bachelor's degree in CS/EE/Math/Stats

Tools

Python

PyTorch

Job description

TikTok is seeking graduates to join the Recommendation Foundation team within Data – Global E-commerce. You will help build event-sequence driven generative recommendation by integrating LLMs, VLMs, and multimodal understanding to push beyond click prediction.

You will participate in training across pre-, mid-, and post-training, design tokenizers, and contribute to end-to-end systems. The role offers bold ideas, growth, and a path to impactful research within a fast-moving, global tech company.