Description
The Quantitative Trading & Research (QTR) group is responsible for systematic trading across various markets.
Within QTR, AI Market Lab combines quantitative research, artificial intelligence, market microstructure, and high-performance engineering to develop electronic trading capabilities.
The role involves leading research on building Transformer-based and time-series foundation models over large-scale market datasets.
Responsibilities:
- Pre-train Transformer-based and time-series foundation models from scratch using large-scale market datasets.
- Develop data representations, tokenization schemes, and model architectures for financial time series.
- Fine-tune and post-train foundation models for alpha generation, pricing, and risk-management tasks.
- Study scaling laws, transfer across instruments, and regime robustness.
- Design evaluation protocols connecting pre-training metrics to economically meaningful outcomes.
- Build reusable training and model-serving components with ML infrastructure engineers.
Required Qualifications:
- Advanced degree in machine learning, computer science, or a related quantitative field.
- Demonstrated experience pre-training large models from scratch.
- Experience building large-scale data pipelines and distributed training systems.
- Deep knowledge of large-model training and evaluation.
- Evidence of research/technical quality through publications or production deployment.
Preferred Qualifications:
- Experience with fine-tuning for forecasting and decision-making.
- Prior work on time-series foundation models or cross-asset transfer learning.
- Experience in quantitative trading or systematic investing.