Algorithm Engineer - AIGC (2027 Graduate)

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:

• Model Training & Optimization: Design and execute training pipelines for large-scale AIGC models, including data curation, training stability, and performance optimization across computation, communication, and storage layers.
• Architecture & System Design: Contribute to the architecture design of video generation models, spanning foundational model training through to post-training alignment and quality enhancement.
• Quality Alignment & Evaluation: Build evaluation pipelines and explore alignment strategies to improve model output quality, including preference alignment and reward modeling.
• Capability Exploration: Investigate and prototype emerging capabilities such as long-form video generation, multi-modal understanding, and inference acceleration techniques.
• Frontier Research: Track cutting-edge AIGC research, drive project planning, and deliver production-grade implementations.

Requirements:

• Currently pursuing or possesses a Masters or above in Computer Science, AI, or a related field from a reputable university with strong academic credentials/results, expected to graduate by May 2027 and join us by June 2027.
• Good publication record at top-tier venues.
• Familiarity with video generation architectures and/or diffusion-based generative models.
• Demonstrated ability to ship research into working systems.
• Strong communication and collaboration skills; comfortable working across research and engineering teams.