AIGC Video Generation Algorithm (Leader)

Department Engineering and Technology

LevelExperienced (Team Lead)

LocationSingapore

The Engineering and Technology team is at the core of the Shopee platform development. The team is made up of a group of passionate engineers from all over the world, striving to build the best systems with the most suitable technologies. Our engineers do not merely solve problems at hand; We build foundations for a long-lasting future. We don't limit ourselves on what we can or can't do; we take matters into our own hands even if it means drilling down to the bottom layer of the computing platform. Shopee's hyper-growing business scale has transformed most "innocent" problems into huge technical challenges, and there is no better place to experience it first-hand if you love technologies as much as we do.

Job Description:

Technical Strategy: Define the AIGC roadmap across pre-training and post-training workstreams — from distributed training infrastructure to alignment, evaluation, and inference deployment.

Pre-training & Infrastructure: Oversee the design of distributed training toolchains for ultra-large-scale AIGC models. Drive system-level optimization across computation, communication, and storage layers. Ensure training stability and efficiency at scale.

Post-training & Alignment: Guide architecture design for video generation post-training — including high-quality instruction data curation, preference alignment (RLHF, DPO, GRPO, PPO), and video quality enhancement pipelines.

Capability Expansion: Push the frontier on long-video modeling, storyline consistency, precise camera control, and multi-modal generation.

Evaluation & Quality: Establish video quality evaluation frameworks and multi-dimensional Reward Models to systematically measure and improve output quality.

Inference & Deployment: Drive model distillation, quantization, and inference acceleration to bring research models into production.

Requirements:

Masters and above in Computer Science or any related field.

At least 5 years of relevant experience in AI/ML, with a strong focus on generative models.

Leadership: Demonstrated experience managing and growing a technical team with direct reports. Track record of hiring, mentoring, and retaining top talent.

AIGC Depth: Hands-on experience in AIGC pre-training OR post-training (or both). Deep familiarity with Transformer architectures and Diffusion models (e.g., Stable Diffusion, Flux, DiT).

Distributed Systems: Strong understanding of distributed training principles (Data/Pipeline/Tensor/Expert Parallelism) and frameworks such as PyTorch, DeepSpeed, and Megatron-LM, OR

Post-training Expertise: Solid grasp of preference alignment methods (RLHF/DPO/GRPO/PPO), fine-tuning techniques (LoRA/QLoRA/DoRA), and distillation approaches (Consistency Models, Flow Matching).

Communication: Excellent cross-functional communication skills. Comfortable presenting to senior leadership and collaborating across engineering, product, and research teams.

Plus Points

Experience building a team or function from zero.

End-to-end ownership of the full lifecycle of a video generation model, from data to deployment.

Research leadership in physical simulation, world consistency, temporal consistency, or causal reasoning.

Expertise in high-quality video evaluation and human preference alignment at scale.

Publications at top-tier venues (NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP).

Familiarity with GPU hardware architecture, CUDA programming, NCCL, and cuDNN.

Experience with extreme efficiency optimization such as inference acceleration, VRAM compression, quantization.

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