AI Research Scientist | Research & Development

Jump Trading Jump Trading · Quant · London, New York City, Singapore · Research and Development

AI Research Scientist at Jump Trading Group, focused on leading LLM research and applications in financial markets. The role involves generating signals from unstructured data, applying ML to achieve state-of-the-art capabilities, and leading research projects from concept to production, with potential for firmwide application. Requires a strong ML background, Python/C++ skills, and experience with ML libraries.

What you'd actually do

  1. leading an open-ended research project from concept to production: finding compelling problems well suited to current and projected LLM capabilities, collaborating extensively with trading teams to understand requirements and constraints, and continuously improving model design, tools, and infrastructure.
  2. apply machine learning to achieve state-of-the-art capabilities in complex and challenging domains.
  3. generate signals from unstructured data.
  4. combine emerging techniques and models with original research.

Skills

Required

  • 5+ year track record of creating ML systems with real metrics & impact in industry and/or academia
  • Strong general ML background with some exposure to language modeling architectures (e.g. transformers, SSMs)
  • Solid development skills in Python and/or C++
  • Familiarity with ML libraries/frameworks such as PyTorch (preferred), TensorFlow, and/or JAX
  • Intellectual curiosity, versatility, and originality combined with a pragmatic outlook
  • Ability to reason through quantitative problems and communicate effectively with trading researchers
  • Reliable and predictable availability

Nice to have

  • Experience with HPC and distributed large model training
  • Experience with GPU performance optimization (CUDA or ROCm)
  • Experience with end-to-end model development, especially in LLMs
  • Prior academic publications and/or contributions to open-source AI research
  • Strong opinions on best practices in ML research, tooling, and/or infrastructure

What the JD emphasized

  • 5+ year track record of creating ML systems with real metrics & impact in industry and/or academia
  • demonstrated ability to apply machine learning to achieve state-of-the-art capabilities
  • leading an open-ended research project from concept to production

Other signals

  • leading LLM research and applications
  • generate signals from unstructured data
  • apply machine learning to achieve state-of-the-art capabilities
  • leading an open-ended research project from concept to production