Algorithm Engineer - AIGC

Responsibilities Model Training & Optimization: Design and execute training pipelines for large-scale AIGC models, including data curation, training stability, and performance optimization. Apply large-scale diffusion models to practical visual tasks such as low-light enhancement. Architecture & System Design: Contribute to the architecture and algorithm design for AIGC and large multi-modality models spanning image and video, from foundational model training through to post-training alignment and quality enhancement. Post-training & Alignment: Implement and advance post-training methods for diffusion-based multimodal large models to improve output quality. Capability Exploration: Investigate and prototype emerging capabilities such as multi-modal understanding and visual content generation. Frontier Research: Track cutting-edge AIGC research, drive project planning, and deliver production-grade implementations. Contribute to the academic community through publications at top-tier venues. Requirements Masters or above in Computer Science, AI, Computer Vision, Applied Mathematics, or a related field from a reputable university with strong academic credentials. Minimum 2 years of full-time working experience in AI Research Experience with multi-modality large models across image or video domains. Good publication record at top-tier venues. Industry experience with large-scale generative models, including diffusion-based architectures and post-training alignment methods, with demonstrated ability to ship research into deployed systems. Familiarity with applying generative models to practical applications such as image/video enhancement, super-resolution, low-light enhancement, or content generation. Strong communication and collaboration skills; comfortable working across research and engineering teams.