About the role#
This internship focuses on the research and development of Omni multimodal large models. You will contribute to the entire lifecycle of model development, from data construction and foundational algorithm design to pre-training, fine-tuning, and reinforcement learning optimization.
What you'll do#
- Design and build training data and foundational model architectures.
- Optimize pre-training, SFT, and RL processes while evaluating model capabilities and exploring downstream applications.
- Analyze R&D challenges and identify performance bottlenecks to accelerate model iteration.
- Research next-generation architectures to advance Omni-modal understanding and generation capabilities.
What you'll need#
- Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields; graduate degrees are prioritized.
- Solid foundation in deep learning algorithms and practical experience in large model development.
- Familiarity with Diffusion Models and Autoregressive Models.
- Proficiency in deep learning network implementation, model tuning, CPU/GPU acceleration, and distributed training/inference optimization.
- Hands-on experience with large-scale multimodal data processing and high-quality data generation.
- Participation in ACM or NOI competitions is highly valued.
- Strong learning agility, communication skills, and teamwork.
Location & details#
- Location: Palo Alto, California
- Work Modality: On-site
- Employment Type: Full-time
- Term: Rolling
