Campus AI Researcher, Phd/postdoc (full-time)

Jump Trading Jump Trading · Quant · London, United Kingdom · Front Office

Research Scientist role focused on applying machine learning to quantitative finance, with an emphasis on implementing research projects from concept to production and improving model design, tools, and infrastructure. Requires a strong publication record and background in deep learning, with bonus points for HPC, distributed training, and GPU optimization.

What you'd actually do

  1. We are seeking research scientists with a demonstrated ability to apply machine learning to achieve state-of-the-art capabilities in complex and challenging domains.
  2. The ideal person for this role will be capable of implementing an open-ended research project from concept to production and continuously improving model design, tools, and infrastructure.
  3. Potential projects may target any area of the quantitative research and monetisation process.
  4. We believe that successful research efforts require a fluid mix of skills including ML expertise, engineering pragmatism, statistics and market intuition.

Skills

Required

  • Strong publication record at ICML, ICLR, AAAI, NeurIPS, UAI, KDD, or equivalent and/or contributions to open-source AI research
  • Strong general ML background with exposure to modern deep learning techniques and/or language modeling architectures (e.g. transformers, SSMs)
  • Solid development skills in Python and/or C++
  • Familiarity with ML libraries/frameworks such as PyTorch, TensorFlow, and/or JAX
  • Intellectual curiosity, versatility, and originality combined with a pragmatic outlook
  • Ability to thrive in a collaborative, team-oriented environment
  • Ability to reason through quantitative problems and communicate effectively with trading researchers
  • Reliable and predictable availability required

Nice to have

  • Experience with HPC and distributed large model training
  • Experience with GPU performance optimisation (CUDA or ROCm)
  • Experience with end-to-end model development
  • Strong opinions on best practices in ML research, tooling, and/or infrastructure

What the JD emphasized

  • Strong publication record at ICML, ICLR, AAAI, NeurIPS, UAI, KDD, or equivalent and/or contributions to open-source AI research
  • end-to-end model development

Other signals

  • implementing an open-ended research project from concept to production
  • continuously improving model design, tools, and infrastructure
  • apply machine learning to achieve state-of-the-art capabilities in complex and challenging domains