We are a small, high-performance trading firm running live market-making strategies in crypto markets (primarily Binance).

We are scaling our research capability and improving how we build, test and refine short-horizon trading signals in production.

Research is tightly linked to live trading performance — ideas are tested quickly and evaluated through real PnL. We value researchers who enjoy understanding why signals work, not just whether they do.

The role

You will focus on developing and improving microstructure signals and components used in market-making strategies.

This is a hands-on quantitative research role working with real market data in a live trading environment. While this is not a strategy ownership position, we are looking for someone who can contribute original thinking, investigate market behaviour and take ownership of their research.

You will work closely with senior researchers and engineers to improve existing systems and identify new signals that enhance trading performance.

What you'll do

1. Build, research and test short-horizon trading signals.
2. Analyse order book dynamics, trade flow and market microstructure.
3. Improve components within existing market-making strategies.
4. Investigate why signals perform, how they decay and when they fail.
5. Run experiments on real market data under live trading constraints.
6. Evaluate signal robustness across different market regimes.
7. Work with engineers to productionise research.

What we're looking for

1. ~4+ years' experience in quantitative research, systematic trading or HFT.
2. Strong understanding of market microstructure and short-horizon market behaviour.
3. Experience researching, developing or improving predictive signals.
4. Strong Python and data analysis skills.
5. Experience working with live trading data, not solely historical backtests.
6. Understanding of execution effects, including slippage, fills and adverse selection.
7. Able to explain research methodology, modelling decisions and the rationale behind their work.

Asset class flexible — crypto, equities, futures, options or proprietary trading backgrounds all considered.

Strong signals

1. Has researched signals used in live or production trading.
2. Demonstrates deep intuition for market microstructure.
3. Understands alpha generation, signal decay and regime dependence.
4. Can distinguish genuine edge from noise.
5. Takes clear ownership of their research and can discuss it in depth.
6. Curious, analytical and comfortable reasoning from first principles.

What this is not

1. Full strategy ownership role.
2. Academic or purely theoretical research.
3. Long-horizon quantitative investing.
4. Data science or machine learning modelling in isolation from trading.

Why join

1. Live trading environment with fast feedback loops.
2. Direct impact on strategies trading real capital.
3. High learning velocity in quantitative research and market microstructure.
4. Close collaboration with experienced researchers and engineers.
5. Opportunity to see research translated quickly into live trading outcomes.