Pay · estimate~$145k – $195kMarket rate · not set by employer
PostedNov 14, 2026
LevelMid level
In short
A leading global multi-strategy hedge fund is seeking a Machine Learning Research Scientist to join a newly formed systematic equities investment team based in New York.
PYTHONPYTORCHMACHINE LEARNING
📋 About the job
A leading global multi-strategy hedge fund is seeking a Machine Learning Research Scientist to join a newly formed systematic equities investment team based in New York. This is a front-office research role focused on the development of proprietary deep learning models for financial time-series data, supporting predictive signal generation and systematic investment strategies.
This position offers the opportunity to shape a long-term research agenda, design bespoke Transformer architectures from first principles and access significant computational resources for large-scale experimentation. The successful candidate will work closely with investment and technology professionals to translate advanced machine learning research into actionable trading signals, operating within a collaborative environment where scientific rigour and independent thinking are highly valued.
✅Key Responsibilities
✅Design and implement custom decoder-only Transformer architectures for financial time-series modelling.
✅Develop tokenisation approaches for intraday market data, incorporating price movements, volume, order flow and cross-sectional features.
✅Build efficient PyTorch training pipelines using mixed-precision training, gradient checkpointing and multi-GPU parallelism.
✅Research attention mechanisms that capture temporal relationships, cross-asset dependencies and patterns across multiple timescales.
✅Develop evaluation frameworks to assess predictive accuracy, signal quality and trading performance.
✅Optimise model inference for low-latency production deployment through compression, quantisation and efficient decoding techniques.
✅Conduct rigorous experiments and ablation studies to validate architectural choices and training methodologies.
✅Collaborate with researchers and developers to integrate model predictions into live trading infrastructure.
✅Define and execute a sustained research agenda, assessing new approaches and refining models through systematic experimentation.
✅Document research methodology, experimental findings and architectural decisions.
🎯Requirements
🔹PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics or a related discipline, with a focus on deep learning.
🔹Demonstrated experience implementing Transformer architectures from first principles.
🔹Deep understanding of attention mechanisms, positional encodings, tokenisation strategies and model training dynamics.