Campus ML Research Engineer (Full-Time)
A graduate ML engineering role in Jump's London office building training and low-latency inference systems for trading models. You work with researchers and quants on large-scale ML infrastructure.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Build reusable financial ML frameworks with researchers
- Optimise training on HPC clusters
- Integrate models into latency-sensitive production systems
- Make ML systems observable and quick to iterate on
What they ask for
Required
- Python and/or C++
- PyTorch, JAX or TensorFlow
- GPU programming (CUDA, Triton, SYCL, ROCm)
- Experience with large-scale ML systems
- Clear written and spoken English
- Education
- Not stated
- Experience
- Campus hire; deep expertise in relevant areas
- Languages and tools
How to prepare
- Write and profile a Triton or CUDA kernel
- Train a model across multiple GPUs and measure scaling
- Build a low-latency inference service
- Study efficient data loading for very large datasets
What to study
- Financial Machine Learning
- Modern C++ for Quantitative Finance
- Trading Systems and Low-Latency Infrastructure
- Quantitative Data Engineering
The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Jump Trading’s site before applying: requirements, location and pay can change after we check them.
View the posting on Jump Trading’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Jump Trading and doesn’t handle applications.