LLM Post-training Engineer Graduate

About the Role

The Multimedia AI team at TikTok focuses on building, researching, and applying Large Language Models (LLMs) to power our global products. Post-training is a critical intersection of research and real-world deployment. As a graduate, you will have opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must commit to an onboarding date by the end of the year.

Key Responsibilities

• Support the development and optimization of post-training strategies, including instruction tuning, preference tuning (SFT/DPO/PPO), and model alignment.
• Assist in building robust evaluation pipelines to measure model performance, helpfulness, and safety across diverse multimedia product use cases.
• Participate in the research and implementation of cutting-edge methodologies in reward modeling and human preference learning.
• Collaborate with engineering teams to bridge the gap between experimental research and production-ready AI applications (e.g., video understanding, translation, and content classification).
• Analyze and process large-scale datasets to identify patterns that improve model behavior and alignment quality.

Requirements & Tech Stack

• Strong programming skills in Python.
• Experience with deep learning frameworks such as PyTorch or JAX.
• Foundational understanding of Transformer architectures and LLM training principles.
• Demonstrated ability to learn quickly in a fast-paced environment.
• Strong passion for AI "landing" and product application.

Qualifications

Minimum Qualifications

• Individuals who are completing or have recently completed a Bachelor's degree in Computer Science, Machine Learning, or a related discipline.

Preferred Qualifications

• Previous experience or research projects involving LLM fine-tuning, RLHF, or synthetic data generation.
• Familiarity with distributed training tools (e.g., DeepSpeed, Megatron-LM).
• Experience or interest in multimodal AI (integrating text with video or audio).
• Proven track record of building and deploying AI projects, either through previous internships, open-source contributions, or academic research.