AI Research Scientist | Research & Development at Jump Trading International Limited

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

11 Feb, 26

Salary

300000.0

Posted On

13 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Language Modeling, Python, C++, PyTorch, TensorFlow, JAX, Quantitative Analysis, Model Development, Collaboration, Engineering Pragmatism, Market Intuition, HPC, GPU Optimization, LLMs, Research, Open-source Contributions

Industry

Financial Services

Description

Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Our AI team is a central R&D group comprised of quantitative researchers, engineers, and ML experts leading LLM research and applications at Jump. Our mission is to combine emerging techniques and models with original research to generate signals from unstructured data. What You’ll Do: 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. The ideal person for this role will be capable of 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. Potential projects may target any area of the quantitative research process with the potential for firmwide application. We believe that successful research efforts require a fluid mix of skills including ML expertise, engineering pragmatism, and market intuition, and welcome all candidates who can demonstrate brilliance in one area, competence in a second, and enthusiasm for the third. Other duties as assigned or needed. Skills You’ll Need: 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 Bonus Points 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 Benefits - Discretionary bonus eligibility - Medical, dental, and vision insurance - HSA, FSA, and Dependent Care options - Employer Paid Group Term Life and AD&D Insurance - Voluntary Life & AD&D insurance - Paid vacation plus paid holidays - Retirement plan with employer match - Paid parental leave - Wellness Programs Annual Base Salary Range $200,000—$300,000 USD

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Responsibilities

Lead open-ended research projects from concept to production, collaborating with trading teams to understand requirements. Continuously improve model design, tools, and infrastructure for quantitative research applications.