ML Research Intern: Finance-Driven, Mentored & Hands-On

Tradermath; Greater London; Hybrid; GBP 18,000 - 28,000; Full time

Job summary

Jane Street in London offers a Machine Learning Research internship that pairs you with mentors and exposes you to real-world trading data. You’ll work on models, algorithms, and systems, evolving from simple approaches to advanced state-space methods.

Throughout the program you’ll attend classes, interactive sessions, and group discussions while gaining hands-on experience using our GPU cluster and educational resources. Publication of results is limited by IP considerations.

Qualifications

• An undergraduate, PhD student, or postdoc with practical experience working on ML problems.
• Able to apply logical and mathematical thinking to all kinds of problems.
• Intellectually curious — asking great questions is more important than knowing all the answers.
• A strong programmer in Python.
• An open-minded thinker and precise communicator who enjoys interacting with colleagues from a wide range of professional backgrounds and areas of expertise.
• Eager to ask questions, admit mistakes, and learn new things.

Responsibilities

• Work with mentors on real-world ML projects.
• Explore approaches from simple models to advanced state-space methods.
• Participate in classes, interactive sessions, and group discussions while applying lessons to practical problems.
• Analyze trading data with access to a GPU cluster and educational resources.

Skills

Python

ML problems

Curiosity

Mathematical thinking

Communication

Collaboration

Education

Undergraduate/PhD student or postdoc

Tools

GPU cluster

Job description

Jane Street in London offers a Machine Learning Research internship that pairs you with mentors and exposes you to real-world trading data. You’ll work on models, algorithms, and systems, evolving from simple approaches to advanced state-space methods.

Throughout the program you’ll attend classes, interactive sessions, and group discussions while gaining hands-on experience using our GPU cluster and educational resources. Publication of results is limited by IP considerations.