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

TikTok is the leading destination for short-form mobile video, and they are seeking a Research Scientist to focus on privacy-preserving large-scale model training and architecture optimization. The role involves designing and optimizing training architectures for generative models while ensuring that privacy is prioritized in technology innovation.

Responsibilities:

• Design and optimize large-scale training architectures for diffusion-based and unified generative models (e.g., DiT, Rectified Flow, hybrid AR + diffusion systems)
• Lead GPU-centric performance optimization, including memory layout, communication overlap, kernel fusion, and throughput scaling across thousands of accelerators
• Develop and evolve distributed training strategies (DP / TP / PP / ZeRO / FSDP-style sharding) tailored to long-running, multi-stage foundation model training
• Build fault-tolerant, self-healing training systems that can sustain long-running jobs under frequent hardware, network, and software failures
• Design mechanisms for fast failure detection, recovery, and minimal training interruption, including checkpointing strategies, restart policies, and controlled rollouts
• Improve training ETTR / MFU / utilization efficiency under real-world production constraints
• Optimize Diffusion Transformer training pipelines, including noise schedules, timestep strategies, and memory-efficient attention mechanisms
• Support unified generation-and-understanding models, enabling shared context, long-sequence multimodal reasoning, and scalable training without architectural bottlenecks
• Collaborate with research teams on architecture-level tradeoffs between quality, compute efficiency, and training stability