Machine Learning Engineer, Multimodal - Intelligent Integrity

Responsibilities

Our Business Integrity team has a strong user focus and a dedication to technical excellence. We aim to meet our users' needs with reliable and high-performing platforms and services. We are looking for strong machine learning engineers who are excited to grow their business understanding, build highly scalable machine learning models, and partner across disciplines with global teams, in pursuit of excellence. Given the fast growth of TikTok in the world, we are working on building a next-generation content understanding system for TikTok monetization. We are seeking Research Engineers who are experienced in machine learning, which can help us create an ecosystem that rewards high quality user experience and advertiser value.

Responsibilities:
1. Focus on ad content understanding and security, conduct algorithm R&D for large model implementation, and apply LLM/MLLM and other AIGC technologies to e-commerce and short-video ad content understanding to build a next-gen large model-based commercial intelligence review system.

2. Deepen multimodal understanding tech for text, audio, video, live streaming, etc., optimize model decision-making for high-accuracy autonomous risk judgment, and implement interpretable CoT generation for traceable model decisions.

3. Explore RL/Agent applications in multimodal review scenarios, track AIGC cutting-edge trends, and deliver algorithm innovation and engineering implementation tailored to commercial business needs.

4. Develop dedicated multimodal content understanding models to empower ad intent recognition, intelligent rule retrieval and accurate risk judgment, enhance advertisers' creation experience, reduce non-compliant content non-detection risks, and protect ad ecosystem security.

Qualifications

Minimum Qualifications:

1. Bachelor's degree or above in CS, AI, Mathematics, Statistics or related majors, with 1+ year of algorithm R&D/project implementation experience in content understanding or AIGC.
2. Solid ML/DL theoretical foundation, in-depth understanding of MLLM/LLM, CV, NLP, multimodal fusion and Agent technologies; strong mathematical skills, excellent self-learning and problem-solving abilities; project implementation experience preferred.
3. Proficient in PyTorch/TensorFlow, with hands-on experience in large model training, fine-tuning and inference deployment; excellent engineering capabilities, master Python/C++.
4. Familiar with technical principles and applications of multimodal large models; experience in content understanding, ad analysis, multimodal representation learning or intelligent review preferred.
5. Accredited publications at CS/ML conferences.