Research Scientist — Privacy-Preserving Large-Scale Model Training & Architecture Optimization

At TikTok, we treat privacy as our top priority in our product design and implementation. 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, and privacy-preserving and compliant training pipelines.

Requirements

• Strong background in large-scale deep learning systems and distributed training.
• Hands-on experience with GPU optimization, including memory management, communication/computation overlap, and performance profiling.
• Experience training diffusion models, DiT-style architectures, or large foundation models at scale.
• Proficiency in PyTorch and modern distributed training stacks.
• Solid understanding of parallelism strategies (DP / TP / PP / ZeRO / FSDP or equivalents).
• Ability to reason about training stability, numerical issues, and long-running job robustness.

Benefits

• Competitive salary and benefits package
• Opportunity to work with a leading technology company
• Collaborative and dynamic work environment
• Professional growth and development opportunities