Machine Learning Software Engineer Graduate (TikTok Content Ecology) - 2027 Start (PhD)

About the Team The Content Ecology team builds next-generation AI products and scalable platform capabilities that power how content is created, understood, discovered, and governed across TikTok globally. Leveraging cutting-edge technologies such as LLMs/MLLMs, multimodal learning, and Agentic AI, the team addresses complex, high-impact challenges across Local Services, Search, Short Drama, Creator Assistance, and AIGC. By uniting frontier AI research with product innovation, our team drives scalable solutions that elevate experiences for users, creators, and merchants worldwide.

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.

Responsibilities: - Develop and optimize LLM/MLLM, Agent, NLP, CV, and recommendation models to improve TikTok’s content ecosystem. - Design, build, and optimize advanced Agentic AI frameworks, focusing on core components like planning, tool use, skills, and memory. - Implement multimodal AI solutions, integrating video, text, and speech understanding. - Train and fine-tune deep learning models using TensorFlow, PyTorch, or other ML frameworks. - Deploy and scale machine learning solutions in a distributed computing environment. - Work closely with AI researchers, software engineers, and business teams to apply AI technologies effectively.

Minimum Qualifications: - Individuals who are completing or have recently completed a PhD degree in computer science, computer engineering, electrical engineering, applied mathematics, or a related discipline. - Strong programming skills in Python, C++, or similar languages. - Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. - Hands-on experience with LLMs/MLLMs, specifically focusing on Post-Training (SFT, RL) and/or Inference optimization. - Solid understanding of machine learning fundamentals and the modern AI stack. - Strong proficiency in integrating AI tools into knowledge discovery and research workflows. - Excellent communication skills to collaborate across teams.

Preferred Qualifications: - Experience with distributed computing and optimizing AI models for real-world applications. - Experience in applying machine learning techniques to enhance business and user experiences. - Deep knowledge of agent architectures, including planning, tool use (e.g., LangChain, LlamaIndex), skills, and memory systems. - Strong experience in working with Agentic modeling, workflows, and frameworks. - Publications in top AI/ML conferences (NeurIPS, ICML, CVPR, ACL, AAAI, etc.) or strong contributions to open-source AI projects.