Our client is a pioneering technology-driven financial services company that leverages AI and ML to deliver high-quality trading solutions. Theyre seeking talented individuals to join their team in advancing research and development in core AI ideal candidate will contribute to breakthrough research in areas such as:
- Advanced model architectures including sparse activation mechanisms and dynamic routing algorithms
- Efficient training and inference methods for large-scale models
- Innovations in attention mechanisms tokenization algorithms and embedding optimization
Key areas of focus include:
- Developing novel approaches to mixture of experts multi-head attention and tokenization
- Analyzing the impact of tokenization on model performance and designing unified frameworks for multilingual models
- Optimizing embedding compression and semantic space optimization
To be considered youll need:
- A Masters or PhD in Computer Science AI Mathematics or a related field
- Research experience in relevant areas with published papers or in-depth projects
- Proficiency in frameworks like PyTorch/JAX and experience with large model training and fine-tuning
- A deep understanding of Transformer architectures and experience with related source code
- Familiarity with distributed training and memory optimization is desirable