Research Scientist Intern (TikTok-Privacy Innovation Lab-Multimodal Generative Model) - 2026 Start (PhD)

About the role

We are building next-generation generative foundation models, with a strong focus on diffusion-based and unified generation-understanding architectures, deployed in privacy-sensitive, production environments. This role sits at the intersection of Large-scale model training systems GPU-first architecture and kernel-level optimization Diffusion / DiT / unified multimodal foundation models Privacy-preserving and compliant training pipelines.

Responsibilities

• You will participate in the architecture design and deep optimization of next-generation text-to-image and text-to-video models, including but not limited to:
  • Develop a deep understanding of and optimize DiT + Flow Matching / Rectified Flow–based generative models
  • Lead or contribute to the design and implementation of: Diffusion Transformer (DiT / MM-DiT) architecture improvements; Unified text-to-image / text-to-video model designs; Latent space, tokenization, and conditioning mechanisms.
  • Perform joint algorithmic and system-level optimization, targeting: Training stability and convergence speed; Memory and compute efficiency; Generation quality and consistency
  • Address challenges in long-sequence, high-resolution, and video generation, including: Efficient attention and temporal modeling strategies; Long-context and long-latent modeling
  • Collaborate closely with systems and kernel engineers to map model designs to efficient implementations
  • Reproduce, analyze, and advance state-of-the-art generative models (beyond simple replication)
• You will work on end-to-end training architecture design, from model-parallel execution and GPU efficiency to robust, fault-tolerant, privacy-aware training infrastructure.

Qualifications

• Minimum Qualifications: Currently pursuing PhD in Computer science, computer engineering, or a related technical discipline. Deep understanding of Diffusion / Flow Matching / Rectified Flow Strong familiarity with DiT / Transformer-based architectures in generative modeling Ability to debug the full pipeline from mathematical formulation → code → training → generated outputs Proficiency with PyTorch and hands-on experience training large-scale models
• Preferred Qualifications: Practical experience with text-to-image or text-to-video models (non-toy systems) Familiarity with multimodal modeling (Text / Image / Video / Audio) Research publications or open-source contributions

Job Information

[For Pay Transparency] Compensation Description (Hourly) - Campus Intern The hourly rate range for this position in the selected city is $60- $60. Benefits may vary depending on the nature of employment and the country work location. Interns have day one access to health insurance, life insurance, wellbeing benefits and more. Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year). Interns who are not working 100% remote may also be eligible for housing allowance. The Company reserves the right to modify or change these benefits programs at any time, with or without notice.