Applied ML Researcher for Trading Systems

Applied ML Researcher for Trading Systems

Full-Time 54000 - 66000 £ / year (est.) No working from home possible
Trading Interview

At a Glance

  • Tasks: Train deep learning models for trading strategies and explore innovative ML techniques.
  • Company: Join Jane Street, a leading firm in trading and technology.
  • Benefits: Competitive salary, mentorship opportunities, and access to cutting-edge resources.
  • Other info: Dynamic team environment with opportunities to present at conferences.
  • Why this job: Shape the future of trading with advanced ML and collaborate with top talent.
  • Qualifications: Strong understanding of machine learning and passion for trading systems.

The predicted salary is between 54000 - 66000 £ per year.

Jane Street is seeking smart, curious people to join our ML team and push the boundaries of trading models.

You will train deep learning models powering our trading strategies while leveraging a large GPU cluster and distributed training workflows.

Our team collaborates across researchers, engineers, and traders, and you will help shape the ML landscape here—exploring LLMs, image models, RL, and classical techniques.

You’ll also mentor teammates and present new ideas at conferences.

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Applied ML Researcher for Trading Systems employer: Trading Interview

Tower Research Capital is an exceptional employer that fosters a dynamic and collaborative work culture, where innovation and rigorous experimentation are at the forefront. Located in a vibrant financial hub, employees benefit from cutting-edge technology and resources, alongside ample opportunities for professional growth and development within the fast-paced world of quantitative trading. Join us to be part of a team that values your contributions and rewards your success in a meaningful way.

Trading Interview

Contact Details:

Trading Interview Recruitment Team

We think you need these skills to ace Applied ML Researcher for Trading Systems

Deep Learning
GPU Computing
Distributed Training Workflows
Machine Learning
Reinforcement Learning (RL)
Large Language Models (LLMs)
Image Models