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

Applications will be reviewed on a rolling basis. We encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

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

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.

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:
1. Develop a deep understanding of and optimize DiT + Flow Matching / Rectified Flow-based generative models
2. 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.
3. Perform joint algorithmic and system-level optimization, targeting: Training stability and convergence speed; Memory and compute efficiency; Generation quality and consistency
4. Address challenges in long-sequence, high-resolution, and video generation, including: Efficient attention and temporal modeling strategies; Long-context and long-latent modeling
5. Collaborate closely with systems and kernel engineers to map model designs to efficient implementations
6. Reproduce, analyze, and advance state-of-the-art generative models (beyond simple replication)

Qualifications

Minimum Qualifications
1. Currently pursuing PhD in Computer science, computer engineering, or a related technical discipline.
2. Deep understanding of Diffusion / Flow Matching / Rectified Flow
3. Strong familiarity with DiT / Transformer-based architectures in generative modeling
4. Ability to debug the full pipeline from mathematical formulation → code → training → generated outputs
5. Proficiency with PyTorch and hands-on experience training large-scale models

Preferred Qualifications
1. Practical experience with text-to-image or text-to-video models (non-toy systems)
2. Familiarity with multimodal modeling (Text / Image / Video / Audio)
3. Research publications or open-source contributions

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy

Client-provided location(s): San Jose, CA

Job ID: TikTok-7602701787133249797

Employment Type: INTERN

Posted: 2026-02-06T19:59:59