Description:
We are seeking interns with solid Computer Science, Machine Learning, Statistics, and Mathematics backgrounds who would be interested in exploring research directions in one of the following general domains:
(i) Large Language Models (LLMs) and Natural Language Processing (NLP)
(ii) Machine Learning (ML) Foundations—including in Architecture, Optimization, Graph Neural Networks (GNN), Multi-Modal Learning (MML), etc.
(iii) Reinforcement Learning (RL)
(iv) Automatic Design Space Exploration (DSE)
(v) Theoretical and/or Empirical Investigation of Statistical Learning Techniques
Responsibilities and opportunities to learn:
Prefer candidates with some of the following qualifications—as related to general domains of focus mentioned above:
- Knowledge of (deep) reinforcement learning, optimization, and search techniques.
- Knowledge of statistical learning—e.g., deep neural networks, sequence processing, graph neural networks, etc.
- Familiarity with ML life cycle, architectures, and model designs.
- Familiarity with ML implementation environments and platforms such as PyTorch and/or Tensorflow.
- Familiarity with distributed system processing, Linux and Python environments, source code management, and team development practices.
- Familiarity with large language models (LLMs) and Visual Language Models (VLMs) is a plus.
Key qualifications:
- Well-organized, detail-oriented, passionate about learning, motivated, and team player.
- Verbal and written communication skills.
- Ph.D. student in Computer Science, Machine/Statistical Learning, Computer Engineering, or closely related majors.