Quant jobs / Jump Trading

Campus AI Research Engineer – Deep Learning (Full-Time)

A graduate research-engineer role applying deep learning to quantitative finance at Jump. You build frameworks and pipelines that help researchers train and deploy large models.

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 quants
Optimise training pipelines for HPC resources
Integrate models into latency-sensitive production systems
Shorten the research iteration loop

What they ask for

Required

Strong ML publications or open-source AI work
Modern deep learning such as transformers
Python and/or C++
PyTorch, JAX or TensorFlow
Quantitative problem-solving

Preferred

HPC and distributed training
CUDA or ROCm optimisation
End-to-end model development

Education: Not stated; research-level ML background expected
Experience: Campus hire
Languages and tools: Python
C++
CUDA
PyTorch
JAX
TensorFlow

How to prepare

Reproduce a transformer or state-space model paper
Train a model across multiple GPUs and profile bottlenecks
Write a CUDA kernel for a custom operation
Test a deep model on noisy time series with strict out-of-sample splits

What to study

The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

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.