About the role#
This is a full-time, on-site internship based in Palo Alto, California. The role focuses on the research and development of Omni multimodal large models, contributing to foundational model design, training optimization, and performance evaluation.
What you'll do#
- Design and construct training data and develop foundational model algorithms.
- Optimize pre-training, Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL) processes.
- Analyze R&D challenges and identify performance bottlenecks to accelerate model development.
- Explore next-generation model architectures and diverse paradigms for multimodal understanding and generation.
- Evaluate model capabilities and explore potential downstream application scenarios.
What you'll need#
- Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Mathematics, or a related field (graduate degrees are prioritized).
- Solid foundation in deep learning algorithms and practical experience in large model development.
- Proficiency in deep learning network implementation, model tuning, CPU/GPU acceleration, and distributed training/inference optimization.
- Hands-on experience in large-scale multimodal data processing and high-quality data generation is highly preferred.
- Familiarity with Diffusion Models and Autoregressive Models is advantageous.
- Participation in ACM or NOI competitions is highly valued.
- Strong learning agility, communication, and teamwork skills.
Location & details#
- Location: Palo Alto, California.
- Modality: On-site.
- Term: Rolling.
- Compensation: This is a paid position with an expected base pay range of $80,168.40 to $124,800.00 per year, depending on knowledge, skills, and experience. Benefits include paid sick leave, 13 paid holidays, and eligibility for the company-sponsored medical plan.
