Algorithm Engineer AIGC
Job Description
Job Description
- 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.
- Master's degree or above in Computer Science or related fields
- First-author publication(s) at top-tier venues (CVPR/ICLR/ECCV/ICML/NeurIPS/TPAMI) on diffusion-based multimodal generation, particularly post-training/alignment methods that improve generation quality;
- Have experience in multimodal large model across image generation or video generation
- Proficient in PyTorch;
- Good programming skills in Python, C++, and CUDA;
- Strong problem-solving skills, innovative thinking, and excellent team collaboration/communication skills.
More Info
Job Type:
Industry:
Employment Type:
About Company
Skills:
Pytorch, C++, Python, Cuda, diffusion-based multimodal generation, post-training alignment methods, large-scale diffusion models, model training optimization
Skills:
Pytorch, C++, Python, Cuda, diffusion-based multimodal generation, post-training alignment methods, architecture and algorithm design for AIGC and large multi-modality models, large-scale diffusion models, model training optimization