Full-time ML engineering role 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 candidates are encouraged to apply. Jump states that it sponsors work visas for full-time positions.
Details
Role information
Location London
Job type Graduate
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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