Hands-on ML Researcher Internship: Real Trading Models

Amsterdam Quant Society; Greater London; Hybrid; GBP 17,000 - 21,000; Full time

Job summary

Jane Street invites applications for a Machine Learning Researcher Internship in London. You will join a team of researchers working on real systematic-trading problems using large-scale market data and state-of-the-art ML techniques.

The internship emphasizes end-to-end research, from dataset exploration to model validation, with direct collaboration among researchers, traders, and engineers. Interns gain exposure to PyTorch-based workflows, learn to reason through non-trivial modelling

Qualifications

• Undergraduate, PhD student, or postdoc with practical ML experience.
• Strong logical and mathematical reasoning across unfamiliar problems.
• Interest in the wider machine-learning landscape and state-of-the-art techniques.
• Comfort working with a broad set of models and practical modelling techniques.
• Ability to implement and iterate quickly in Python using a preferred ML framework.
• Willingness to ask questions, acknowledge mistakes, and learn quickly.
• Fluency in English.

Responsibilities

• Work on real systematic-trading problems using large-scale market data.
• Experiment with new modelling approaches on trading-related tasks.
• Explore open-ended ML approaches when the best solution is not yet known.
• Work directly with market data and tune hyperparameters.
• Analyze model predictions and decide next validation steps.
• Document and present results from experiments.

Skills

Python

Deep learning

Machine learning

Research

Fluency in English

Education

Undergraduate, PhD student, or postdoc

Tools

PyTorch

Job description

Jane Street invites applications for a Machine Learning Researcher Internship in London. You will join a team of researchers working on real systematic-trading problems using large-scale market data and state-of-the-art ML techniques.

The internship emphasizes end-to-end research, from dataset exploration to model validation, with direct collaboration among researchers, traders, and engineers. Interns gain exposure to PyTorch-based workflows, learn to reason through non-trivial modelling