Research Scientist Intern - Large-Scale Machine Learning Systems (SysML) - Global Tech Research Program - 2027 Start (PhD)
Tasks
- Apply heterogeneous hardware for multimodal recommendation systems
- Balance stability and efficiency for end to end long sequence modeling
- Build high performance multimodal inference engines
- Design multimodal co training architectures
- Develop large scale multimodal recommendation models
- Develop multimodal training frameworks
- Implement mixed precision and sparse MoE
- Improve recommendation accuracy and generalization
- Optimize training and inference for ultra large models
- Unify multimodal representations and fusion
Perks/Benefits
Skills/Tech-stack
Computer Vision | End to End | End-to-end modeling | Federated Learning | Graph computing | Heterogeneous hardware | Inference Optimization | Language Processing | Long Sequence Modeling | MOE | Machine Learning | Memory Networks | Mixed Precision | Mixture of Experts | Multimodal Learning | Natural Language | Natural Language Processing | PyTorch | Python | Recommendation Systems | Sequence Modeling | Sparse Mixture of Experts | Training Optimization
Education
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