LLM Post-Training Engineer, Research & Product

Description

About the Team
Would you like to play a part in shipping groundbreaking technology for Large Language Models and Artificial Intelligence? Join the Multimedia AI team at TikTok! We build, research, and apply LLMs to power our products and impact millions of users.

We believe the most interesting and impactful problems in AI arise at the intersection of research and real-world deployment — and post-training is where that intersection is sharpest. You will work with a close-knit team of world-class engineers and scientists to tackle some of the most challenging problems in aligning and improving foundation models. You will have unique opportunities to identify and develop novel post-training techniques that directly improve the experience of millions of users globally.

Responsibilities：
As an LLM Post-training Engineer, you will play a critical role in driving post-training strategies across various LLMs and product areas. Your work will directly shape the behavior, safety, and quality of our foundation models.
- Lead efforts in instruction tuning, preference tuning, and model alignment to ensure models are helpful, safe, and performant in real-world applications.
- Drive technical roadmaps and propose your own research agenda to advance the state-of-the-art in post-training methodologies.
- Tackle complex problems in reward modeling, human preference learning, and the evaluation of model behavior across diverse product use cases.
- Provide technical mentorship and guidance to the team, fostering a culture of engineering excellence and rapid innovation.
- Collaborate closely with cross-functional engineering and product teams to integrate advanced LLM capabilities into the TikTok ecosystem.
- Drive execution on large-scale training pipelines and optimization strategies to bridge the gap between theoretical research and production-ready AI.

Requirements

Minimum Qualifications:
- BS or MS in Machine Learning, Computer Science, or a related technical field.
- Demonstrated expertise in bridging the gap between research and product innovation.
- Proven and consistent track record of forming partnerships to solve complex technical problems at scale, ideally for products with a global customer base.
- Strong proficiency in deep learning frameworks and experience with large-scale distributed training.

Preferred Qualifications:
- Specialized expertise in LLM Post-training.
- Experience with reward model design, evaluation benchmarks, and red-teaming for LLM safety.
- Contributions to the research community or significant contributions to open-source LLM projects.
- Ability to thrive in a fast-paced environment and manage evolving priorities.