Campus ML Research Engineer (Intern)

Jump Trading is recruiting for Campus ML Research Engineer (Intern) in London. This listing was last seen on the firm's own job board on August 22, 2026.

Low-latency engineering is the product, so the systems bar is higher than the probability bar.

What sets Jump Trading apart: Latency engineering weighted above trading intuition. Deep C++ and hardware questions, including cache and network behaviour. Secretive by policy, so very little public information about desks.

Related preparation: Quantitative Researcher (QR) Career Roadmap, Machine Learning Quant Career Roadmap, Quant Strategist (Desk Quant) Career Roadmap, Quant Data Scientist (Alternative Data) Roadmap, Quant Research Ops (MLOps) Roadmap and Financial Data Scientist Career Roadmap.

The role, as Jump Trading describes it, published on July 13, 2026 and reproduced from their job board:

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…

Our trading teams are each comprised of a dynamic group of traders, quantitative researchers, and engineers who work together to examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models.

We are seeking world-class engineers to collaborate with our research, trading, engineering teams to build state-of-the-art ML systems that solve some of the most complex problems in quantitative finance. Whether optimizing training pipelines on high-performance computing clusters, developing low-latency inference systems, or pushing the boundaries of AI research from concept to production, you'll have the opportunity to work on impactful projects in a fast-paced, collaborative environment. If you are driven by technical challenges, eager to work with large-scale systems, and passionate about advancing ML capabilities, we want to meet you.

What you'll do:

Apply state-of-the-art techniques to complex and challenging domains.

Work closely with researchers and quants to build flexible and reusable frameworks for financial ML.

Optimize training pipelines to make the best use of our HPC resources.

Integrate ML models into production systems where latency matters.

Work across a mix of programming languages: C / C++ / Python / CUDA and other low-level GPU languages.

Build large scale ML systems that are observable, performant, and flexible. Help improve productivity by reducing the iteration cycle time on research.

Other duties as assigned or needed.