LLM Post-training Engineer Graduate (Research & Product) - 2027 Start

About the team

The Multimedia AI team at TikTok focuses on building, researching, and applying Large Language Models (LLMs) to power our global products. We believe the most impactful problems in AI arise at the intersection of research and real-world deployment—and post-training is where that intersection is sharpest. We are looking for talented individuals to join our team.

As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

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.

Minimum Qualifications: - Individuals who are completing or have recently completed a Bachelor's degree in Computer Science, Machine Learning or a related discipline. - Strong programming skills in Python and 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 and a strong passion for AI "landing" and product application.

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. By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: