Two Sigma is looking for creative experts who are interested in applying general Machine Learning and specifically Deep Learning, Large Language Models, and Reinforcement Learning techniques to many types of problems in complex systems, and particularly those with large amounts of noisy data.
Requirements
- Excellent programming skills in Python (familiarity with Rust/Java is a plus) and deep knowledge of Tensorflow and/or PyTorch
- Internships/work/course experience using Deep Learning, LLMs and/or Reinforcement Learning
- Preferably practical experience writing and using data pipelines to handle large amounts of noisy data for machine learning problems
- Preferably some relevant research experience (that may have led to publications at NeurIPS, ICML, ICLR or similar)
- Understanding of basic statistics
- Experience with cloud computing environments and multi-machine setups
Responsibilities
- Develop effective techniques and/or infrastructure for a specific project/idea under guidance of an experienced member of the team over a time of roughly 10 weeks during the summer
- In our research environment, you will write code, use the latest AI and machine learning tools, run experiments, discuss approaches and results with others, and generally develop techniques and processes to help improve our understanding of how financial data influences the world around us
Other
- This role is only open to MS with work experience and PhDs, both in their penultimate year of study.
- Working towards a degree in Computer Science, Engineering, or other STEM field, preferably in a PhD program, or in a Master’s program with some prior work experience
- Curiosity and interest in learning about financial data modeling in a collaborative environment
- We are proud to be an equal opportunity workplace. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity/expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristics.
