2021 AI Research - Off-Cycle Internship
As an intern on the team, you will conduct end-to-end research typically within a specialised focus area. You will work on multiple research projects in collaboration with internal and external researchers and with applied engineering teams.
You will be integral to all aspects of the research lifecycle such as formulating problems, gathering data, generating hypotheses, developing models and algorithms, conducting experiments, synthesizing results, building prototype applications and communicating the significance of your research.
Your output will result in high-impact business applications, open source software, patents and/or publications in AI/ML conferences and journals. As a member of the AI research community, you will also have the opportunity to participate in relevant top-tier academic conferences to broaden the impact of your contributions.
- Enrolled in a PhD in Computer Science (especially AI/ML) or related fields
- Research publications in prominent AI/ML venues; e.g., conferences, journals
- Strong expertise in one or more specialized areas; e.g., deep learning (DL), Reinforcement learning (RL), Explainable AI (XAI), planning, information representation and retrieval, graphs, multiagent systems (MAS), natural language processing (NLP)
- Practical experience with ML platforms such as Tensorflow/Keras, PyTorch, etc.
- Comfort with rapid prototyping and disciplined software development processes
- Practical software engineering experience in collaborative project settings
- Enrolled in a Master’s degree in Computer Science, Statistics, Engineering or related fields
- Extensive programming skills in Python, Java or C++
- Proficient understanding of fundamental AI and ML techniques; e.g., A*, regularization
- Practical experience with statistical data analysis and experimental design
- Curiosity, creativity, resourcefulness and a collaborative spirit
- Clear and effective verbal and written communication skills
- Demonstrated ability to work on multi-disciplinary teams with diverse backgrounds
- Interest in problems related to the financial services domain (specific past experience in the domain is not required)