Building scalable research platforms to be used across multiple asset classes and trading strategies, influencing trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.
Requirements
- 5+ years of experience in designing research platforms in trading environments
- Previous experience working with ML methodologies and frameworks such as LLMs, Deep Learning, Neural Networks, etc.
- Software development experience in C++ or Java + proficiency in Python
- Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
- Hands-on experience with ML pipelines in high-performance (real-time, low latency) environments is a strong plus
Responsibilities
- Develop high-throughput, scalable research platforms, with a focus on the interaction between data, ML pipelines and back testing
- Guide tooling to facilitate unconstrained experimentation at a large scale; generalize tooling across asset classes, horizons and trading strategies
- Collaborate with quantitative researchers and traders to investigate and evaluate research ideas and to develop scientific libraries to share findings
- Evaluate and roll out third-party tooling (e.g., MLflow; Neptune; Ray); lead implementation and optimization of our research tools
- Create efficient processes for reproducible research
- Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
Other
- Collaborate with quantitative traders and researchers to evaluate and implement new research ideas and approaches across the organization.
- Collaborate with quantitative researchers and traders to investigate and evaluate research ideas and to develop scientific libraries to share findings
