About the role#
The Hunyuan Multimodal Algorithm Researcher Intern will join the Technology Engineering Group to contribute to the research and development of Omni multimodal large models. This role focuses on pushing the boundaries of multimodal understanding and generation through scientific analysis and architectural innovation.
What you'll do#
- Conduct research and development for Omni multimodal large models, including data construction, foundational algorithm design, and optimization across pre-training, SFT, and RL.
- Analyze R&D challenges to identify performance bottlenecks and implement solutions to accelerate model development.
- Explore next-generation model architectures and diverse paradigms to advance multimodal capabilities.
- Evaluate model performance and investigate potential downstream application scenarios.
What you'll need#
- Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Mathematics, Data Science, Electrical Engineering, Computer Engineering, Physics, or Statistics; graduate degrees are prioritized.
- Solid foundation in deep learning algorithms with practical experience in large model development.
- Proficiency in deep learning network implementation, model tuning, CPU/GPU acceleration, and distributed training/inference optimization.
- Familiarity with Diffusion Models and Autoregressive Models.
- Hands-on experience with large-scale multimodal data processing and high-quality data generation.
- Participation in ACM or NOI competitions is highly valued, as is experience in cross-modal research or publications in top-tier conferences.
Location & details#
- Location: Palo Alto, California.
- Modality: On-site.
- Employment: Full-time internship with rolling start dates.
