ML engineering internship building financial-ML frameworks, HPC training pipelines, low-latency inference, and large-scale GPU systems with Python, C++, CUDA, and modern ML libraries.
- Build reusable frameworks for financial machine learning.
- Optimise training pipelines for high-performance computing.
- Integrate ML models into latency-sensitive production systems.
- Build large-scale, observable ML systems.
- Work with C, C++, Python, CUDA, and other low-level GPU technologies.
- Python and/or C++.
- PyTorch, JAX, TensorFlow, or another deep-learning library.
- GPU programming with CUDA, Triton, SYCL, ROCm, or similar tools.
- Large-scale ML systems, including very large training datasets and low-latency or high-throughput inference.
- Strong written and verbal English.
- Creativity, self-motivation, collaboration, and reliable availability.
Jump does not expect every candidate to have every listed skill. International students are encouraged to apply.
Details
Role information
Location London
Job type Internship
Category Machine Learning
Company
Chicago, United States
Global quantitative trading firm combining research, advanced engineering, AI/ML, and high-performance infrastructure across markets and time horizons.
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