Campus AI Researcher, Phd/postdoc (intern)

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

Seeking a research scientist with a demonstrated ability to apply machine learning to achieve state-of-the-art capabilities in complex and challenging domains. The ideal candidate will be capable of implementing an open-ended research project from concept to production and continuously improving model design, tools, and infrastructure. Potential projects may target any area of the quantitative research and monetisation process. Requires a strong publication record and background in deep learning techniques and/or language modeling architectures.

What you'd actually do

  1. implementing an open-ended research project from concept to production
  2. continuously improving model design, tools, and infrastructure
  3. apply machine learning to achieve state-of-the-art capabilities in complex and challenging domains
  4. examine the global markets, seeking to understand the complexities of various traded products and exchanges
  5. leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models

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

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
  • Reliable and predictable availability required

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