HFT Researcher
⚲ Amsterdam / Limassol
About the Role
We are expanding our HFT research team and launching a new direction focused on tokenized equities and other traditional assets traded on crypto-native exchanges.
We are looking for a Quantitative Researcher who combines strong quantitative research skills with a deep understanding of crypto market structure and traditional financial markets.
Responsibilities
Research, develop, and validate quantitative trading strategies for tokenized equities and other assets on crypto-native exchanges.
Identify trading opportunities arising from differences between traditional and tokenized markets, including overnight trading dynamics.
Develop predictive models and apply statistical and machine learning techniques to HFT/MFT trading strategies.
Analyze market microstructure, exchange mechanics, and trading behavior to identify potential sources of alpha.
Design research experiments, test hypotheses, and evaluate strategy performance and robustness.
Independently generate research ideas and contribute to the development of a new trading direction.
About the Team
We are building a research lab first — and a proprietary investment fund around it. The core belief is that long-term trading edge comes from a systematic ability to generate, test, and compound research insights faster and more rigorously than the market.
The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI.
You might thrive in this role if you have
3+ years of relevant experience in quantitative research or algorithmic trading, with a strong track record of hands-on research.
Experience developing quantitative trading models and strategies in crypto or traditional financial markets.
Strong understanding of market microstructure and systematic trading.
Solid foundation in probability, statistics, and applied mathematics.
Practical understanding of machine learning techniques and their application to financial markets.
Strong research methodology, including hypothesis formulation, model validation, and experimental design.
Ability to work independently, challenge assumptions, and drive research initiatives.
Nice to HaveDeep understanding of Hyperliquid, including its exchange architecture, matching engine, and consensus mechanism.
Experience trading US equities or commodities at a hedge fund, proprietary trading firm, or investment bank.
Experience developing overnight trading strategies or strategies exploiting differences between traditional and tokenized markets.
Experience with high-frequency or mid-frequency trading strategies.
Practical experience applying machine learning techniques to financial modeling and signal extraction.
HFT Researcher
⚲ Amsterdam / Limassol
About the Role
We are expanding our HFT research team and launching a new direction focused on tokenized equities and other traditional assets traded on crypto-native exchanges.
We are looking for a Quantitative Researcher who combines strong quantitative research skills with a deep understanding of crypto market structure and traditional financial markets.
Responsibilities
Research, develop, and validate quantitative trading strategies for tokenized equities and other assets on crypto-native exchanges.
Identify trading opportunities arising from differences between traditional and tokenized markets, including overnight trading dynamics.
Develop predictive models and apply statistical and machine learning techniques to HFT/MFT trading strategies.
Analyze market microstructure, exchange mechanics, and trading behavior to identify potential sources of alpha.
Design research experiments, test hypotheses, and evaluate strategy performance and robustness.
Independently generate research ideas and contribute to the development of a new trading direction.
About the Team
We are building a research lab first — and a proprietary investment fund around it. The core belief is that long-term trading edge comes from a systematic ability to generate, test, and compound research insights faster and more rigorously than the market.
The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI.
You might thrive in this role if you have
3+ years of relevant experience in quantitative research or algorithmic trading, with a strong track record of hands-on research.
Experience developing quantitative trading models and strategies in crypto or traditional financial markets.
Strong understanding of market microstructure and systematic trading.
Solid foundation in probability, statistics, and applied mathematics.
Practical understanding of machine learning techniques and their application to financial markets.
Strong research methodology, including hypothesis formulation, model validation, and experimental design.
Ability to work independently, challenge assumptions, and drive research initiatives.
Nice to HaveDeep understanding of Hyperliquid, including its exchange architecture, matching engine, and consensus mechanism.
Experience trading US equities or commodities at a hedge fund, proprietary trading firm, or investment bank.
Experience developing overnight trading strategies or strategies exploiting differences between traditional and tokenized markets.
Experience with high-frequency or mid-frequency trading strategies.
Practical experience applying machine learning techniques to financial modeling and signal extraction.
HFT Researcher
⚲ Amsterdam / Limassol
About the Role
We are expanding our HFT research team and launching a new direction focused on tokenized equities and other traditional assets traded on crypto-native exchanges.
We are looking for a Quantitative Researcher who combines strong quantitative research skills with a deep understanding of crypto market structure and traditional financial markets.
Responsibilities
Research, develop, and validate quantitative trading strategies for tokenized equities and other assets on crypto-native exchanges.
Identify trading opportunities arising from differences between traditional and tokenized markets, including overnight trading dynamics.
Develop predictive models and apply statistical and machine learning techniques to HFT/MFT trading strategies.
Analyze market microstructure, exchange mechanics, and trading behavior to identify potential sources of alpha.
Design research experiments, test hypotheses, and evaluate strategy performance and robustness.
Independently generate research ideas and contribute to the development of a new trading direction.
About the Team
We are building a research lab first — and a proprietary investment fund around it. The core belief is that long-term trading edge comes from a systematic ability to generate, test, and compound research insights faster and more rigorously than the market.
The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI.
You might thrive in this role if you have
3+ years of relevant experience in quantitative research or algorithmic trading, with a strong track record of hands-on research.
Experience developing quantitative trading models and strategies in crypto or traditional financial markets.
Strong understanding of market microstructure and systematic trading.
Solid foundation in probability, statistics, and applied mathematics.
Practical understanding of machine learning techniques and their application to financial markets.
Strong research methodology, including hypothesis formulation, model validation, and experimental design.
Ability to work independently, challenge assumptions, and drive research initiatives.
Nice to HaveDeep understanding of Hyperliquid, including its exchange architecture, matching engine, and consensus mechanism.
Experience trading US equities or commodities at a hedge fund, proprietary trading firm, or investment bank.
Experience developing overnight trading strategies or strategies exploiting differences between traditional and tokenized markets.
Experience with high-frequency or mid-frequency trading strategies.
Practical experience applying machine learning techniques to financial modeling and signal extraction.
