Research Intern, Efficient ML Engineer (AI Platform)- 2026 Start (PhD)

Responsibilities

• Develop algorithm acceleration technologies for text-to-image/text-to-video models through knowledge distillation, model architecture redesign (dynamic MoE routing/sparse attention), and parameter-efficient design (low-bit quantization) to achieve order-of-magnitude efficiency gains.
• Lead generative model innovation with focus on diffusion acceleration (sampling step reduction/latent optimization), autoregression model efficiency.
• Collaborate cross-functionally to identify performance bottlenecks, optimize vision models via algorithmic breakthroughs, and enhance TikTok's product capabilities.

Qualifications

• Currently pursuing a PhD in Computer Science, engineering or quantitative field
• Proficient in C++/Python and high-performance coding.
• Expertise in diffusion models (Stable Diffusion/DiT) with deep understanding of computational bottlenecks and optimization methodologies.
• Proven experience in ≥1 domain: model compression (quantization/knowledge distillation), efficient architectures (MoE/sparse attention), generative alignment (RLHF/DPO).
• Excellent communication and teamwork skills, capable of thriving in a fast-paced work environment.

Preferred Qualifications

• Kaggle competition achievements, publications at ICML/NeurIPS/CVPR, or open-source contributions (e.g., HuggingFace Diffusers optimization).
• Research experience in GenAI /MLsys areas.
• Familiarity with open source deep learning frameworks such as Pytorch/DeepSpeed/Jax etc.