Machine Learning Researcher - HFT

The team trades at sub-second to intraday horizons across Asian equity markets, where signal decay is fast, capacity is tightly constrained and execution quality is inseparable from alpha. This is a seat for a researcher who can build predictive models under hard latency and microstructure constraints - and see them go live quickly.Key Responsibilities

Develop machine learning models for short-horizon price prediction at tick, sub-second and intraday frequencies.

Research order book dynamics - queue position, order flow imbalance, liquidity provision and adverse selection - and convert them into tradeable signals.

Engineer features directly from full-depth order book, trade-by-trade and nanosecond-timestamped market data.

Build models that respect real-world constraints: latency budgets, exchange throttles, fill probability, market impact and transaction costs.

Own the research lifecycle end to end, from hypothesis through to production deployment and live monitoring of model decay.

Work in tight partnership with quantitative developers and execution engineers, where research and infrastructure are inseparable.

Requirements

PhD or Master's in Machine Learning, Statistics, Computer Science, Mathematics, Physics or a related quantitative discipline from a top-tier university.

Strong applied ML background with genuine understanding of low signal-to-noise, high-frequency data - including online learning, regularisation, and robustness to regime shifts.

Excellent

Python

and strong

C++

- this is a latency-sensitive environment where research code sits close to production.

Demonstrated experience with high-frequency market data at scale: L2/L3 order book reconstruction, tick data handling, and clock/timestamp discipline.

Rigorous approach to backtesting HFT strategies, including realistic fill simulation and slippage modelling.

Desired Skills and Experience

A leading global HFT trading house is expanding its systematic equities platform in Singapore and is hiring a Machine Learning Researcher into a high frequency statistical arbitrage effort.

The team trades at sub-second to intraday horizons across Asian equity markets, where signal decay is fast, capacity is tightly constrained and execution quality is inseparable from alpha. This is a seat for a researcher who can build predictive models under hard latency and microstructure constraints - and see them go live quickly.

Key Responsibilities

Develop machine learning models for short-horizon price prediction at tick, sub-second and intraday frequencies.

Research order book dynamics - queue position, order flow imbalance, liquidity provision and adverse selection - and convert them into tradeable signals.

Engineer features directly from full-depth order book, trade-by-trade and nanosecond-timestamped market data.

Build models that respect real-world constraints: latency budgets, exchange throttles, fill probability, market impact and transaction costs.

Own the research lifecycle end to end, from hypothesis through to production deployment and live monitoring of model decay.

Work in tight partnership with quantitative developers and execution engineers, where research and infrastructure are inseparable.

Requirements

PhD or Master's in Machine Learning, Statistics, Computer Science, Mathematics, Physics or a related quantitative discipline from a top-tier university.

Strong applied ML background with genuine understanding of low signal-to-noise, high-frequency data - including online learning, regularisation, and robustness to regime shifts.

Excellent Python and strong C++ - this is a latency-sensitive environment where research code sits close to production.

Demonstrated experience with high-frequency market data at scale: L2/L3 order book reconstruction, tick data handling, and clock/timestamp discipline.

Rigorous approach to backtesting HFT strategies, including realistic fill simulation and slippage modelling.