Machine Learning Research Scientist — New York, NY

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.

• Expert-level PyTorch skills, including custom modules, training loops, mixed-precision training and multi-GPU training.

• Experience training large-scale models with 100 million or more parameters.

• Strong mathematical foundations in linear algebra, probability, optimisation and information theory.

• Strong programming skills in Python and C++ for performance-critical components.

• Ability to independently define and execute a research agenda spanning several months.

• Familiarity with AI-assisted development tools.

• Experience applying deep learning to financial data or time-series forecasting, including tokenisation of continuous or non-text data, is advantageous.

• Published research at leading machine learning conferences, such as NeurIPS, ICML or ICLR, or equivalent industry research experience is preferred.

• Knowledge of market microstructure, intraday trading dynamics, model compression and inference optimisation is beneficial.

For more information contact:

8g58Qa rbtBDc – ••••••••