Efficient ML Graduate; AI Platform PhD

Position: Efficient ML Graduate (AI Platform)- 2026 Start (PhD)
TEAM INTRODUCTION The Vision Engineering Team at Tik Tok is at the forefront of delivering GenAI technologies directly into the Tik Tok products worldwide. Leveraging our proprietary AI infrastructures, we streamline the creation, integration, testing, and deployment of the GenAI features. This also encompasses large-scale training stability and optimization for acceleration, as well as large model inference and multi-machine multi-card deployment.

Our work enhances user experience by powering diverse functionalities, including visual enhancements, video editing tools, and creative camera filters, both within Tik Tok and other applications. We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite cessful candidates must be able to commit to an onboarding date by end of year 2026.

Please state your availability and graduation date clearly in your resume. 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 Byte Dance's product capabilities.

Minimum qualifications - Final year Ph.D or recent Ph.D graduates 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., Hugging Face Diffusers optimization).

- Research experience in GenAI /MLsys areas.

- Familiarity with open source deep learning frameworks such as Pytorch/Deep Speed/Jax etc. By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: