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.

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.