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About the Role
Our goals are to give you a real sense of what it's like to work at Jane Street as a Machine Learning Engineer while also providing a truly unparalleled educational experience. You'll be paired with full-time employees who act as mentors, collaborating with you on real-world ML projects we actually need done. Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of Jane Street's global business. Our ever-changing trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction. During the programme, you'll work on projects mentored closely by the full-time employees who designed them. Some projects consider big-picture questions that we're still trying to figure out, while others involve building something new. You will get access to our growing GPU cluster containing thousands of H100/H200/B200s and gain an understanding of the differences between textbook machine learning and its application to noisy financial data.
Responsibilities
- Collaborate with full-time employees on real-world ML projects.
- Work on projects mentored by full-time employees.
- Build new ML applications or address big-picture questions.
Requirements
- An undergraduate, postgraduate or PhD student with practical experience training an ML model, working on an ML library, or optimising an ML workflow.
- A top-notch programmer with a love for technology.
- Intellectually curious, collaborative, and eager to learn.
- Humble and unafraid to ask questions and admit mistakes.
- Fluent in English.
Skills
- Machine Learning
- Programming
- ML model training
- ML library development
- ML workflow optimization
Experience Level
- Student
Education Level
- Undergraduate
- Postgraduate
- PhD
About the Company
- Machine learning is a critical pillar of Jane Street's global business.
- Our ever-changing trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction.
- We are more interested in how you think and learn than what you currently know.