Quant Researcher (Market Making / HFT)- Onsite/ Remote

The context

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 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.

The role

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

This is a hands-on research role focused on real market data, not strategy ownership.

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

What you’ll do

1. Build and test short-horizon trading signals
2. Analyse order book and trade flow behaviour
3. Improve components within existing market-making strategies
4. Run experiments on real market data under live constraints
5. Help evaluate signal robustness and decay
6. Work with engineers to productionise research

What we’re looking for

1. ~4+ years experience in quant research, trading, or HFT
2. Exposure to microstructure / short-horizon trading
3. Strong Python or data analysis skills
4. Experience working with real trading data (not just backtests)
5. Understanding of execution effects (slippage, fills, adverse selection)

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

Strong signals

1. Has worked on signals used in live or production trading
2. Strong intuition for market microstructure behaviour
3. Understands signal decay and regime dependence
4. Can separate noise from the real edge

What this is not

1. Strategy design / system ownership role
2. Academic or research-only position
3. Long-horizon quant investing role
4. Data science / ML-only modelling role

Why join

1. Live trading environment with fast feedback loops
2. Direct impact on strategies trading real capital
3. High learning velocity in microstructure trading
4. Close collaboration with senior researchers