Quant jobs / Jump Trading

Campus AI Research Engineer – Deep Learning (Intern)

An internship version of Jump's deep learning research-engineer role. Interns help build ML frameworks and training pipelines used by trading researchers.

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.

Prototype reusable financial ML components
Speed up a training pipeline on HPC resources
Test model integration into latency-sensitive systems
Present results to researchers

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 intern
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.