Summary

Lead quantitative research on Transformer-based and time-series foundation models for systematic trading across financial markets. The role focuses on large-scale pre-training, post-training, evaluation, and model-serving infrastructure using PyTorch or JAX, with applications in forecasting, pricing, market making, execution, and risk management.

Responsibilities

• Pre-train foundation models from scratch
• Develop representations and tokenization schemes
• Design distributed training recipes
• Fine-tune models for trading tasks
• Evaluate robustness across regimes
• Build training and model-serving components