HFT Researcher
⚲ Netherlands / Remote
About the Role
As an HFT Researcher, you will join our research team and shift the paradigm of short-horizon systematic trading — from manual strategy research to scalable, agent-driven research systems. Instead of directly building trading strategies, you will focus on encoding your expertise into autonomous agents that generate, test, and optimize strategies at scale. You will operate at the intersection of market microstructure, modeling, and system design, transforming research into a continuously learning pipeline.
Formalize the research process for short-horizon trading by defining hypothesis spaces, validation logic, and the full lifecycle from idea to evaluation
Design and build agent-driven research systems that autonomously generate, test, and optimize trading strategies
Translate market microstructure intuition into machine-executable features, signals, and constraints that guide agent behavior
Develop and adapt backtesting and simulation frameworks to support large-scale, autonomous experimentation under realistic execution conditions
Optimize research scalability by increasing throughput of hypothesis generation, balancing exploration vs exploitation, and ensuring statistical robustness
Collaborate with engineering to integrate agent-based research systems into production trading pipelines and continuously improve their performance
You’ll work at the frontier of real-world ML, with freedom to define problems, test ideas, and push them into live trading systems.
About the Team
We are building a large, proprietary hedge fund where technology is the core advantage. The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI. The fund operates tightly with an internal AI Research Lab; together we design and own the full stack: from research ideas to production trading systems.
You might thrive in this role if you have
1+ year of experience in HFT or short-horizon systematic trading, with a clear understanding of how strategies are researched, validated, and deployed
Strong understanding of market microstructure, exchange mechanics, and execution constraints
Solid grounding in probability, statistics, and optimization, with the ability to apply them in noisy, real-world settings
Strong programming skills in Python and ML frameworks, with the ability to write efficient, clean, and scalable code
Ability to formalize and decompose the research process into structured, repeatable components suitable for automation
Motivation to shift from manual research to building and scaling agent-driven research systems
HFT Researcher
⚲ Netherlands / Remote
About the Role
As an HFT Researcher, you will join our research team and shift the paradigm of short-horizon systematic trading — from manual strategy research to scalable, agent-driven research systems. Instead of directly building trading strategies, you will focus on encoding your expertise into autonomous agents that generate, test, and optimize strategies at scale. You will operate at the intersection of market microstructure, modeling, and system design, transforming research into a continuously learning pipeline.
Formalize the research process for short-horizon trading by defining hypothesis spaces, validation logic, and the full lifecycle from idea to evaluation
Design and build agent-driven research systems that autonomously generate, test, and optimize trading strategies
Translate market microstructure intuition into machine-executable features, signals, and constraints that guide agent behavior
Develop and adapt backtesting and simulation frameworks to support large-scale, autonomous experimentation under realistic execution conditions
Optimize research scalability by increasing throughput of hypothesis generation, balancing exploration vs exploitation, and ensuring statistical robustness
Collaborate with engineering to integrate agent-based research systems into production trading pipelines and continuously improve their performance
You’ll work at the frontier of real-world ML, with freedom to define problems, test ideas, and push them into live trading systems.
About the Team
We are building a large, proprietary hedge fund where technology is the core advantage. The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI. The fund operates tightly with an internal AI Research Lab; together we design and own the full stack: from research ideas to production trading systems.
You might thrive in this role if you have
1+ year of experience in HFT or short-horizon systematic trading, with a clear understanding of how strategies are researched, validated, and deployed
Strong understanding of market microstructure, exchange mechanics, and execution constraints
Solid grounding in probability, statistics, and optimization, with the ability to apply them in noisy, real-world settings
Strong programming skills in Python and ML frameworks, with the ability to write efficient, clean, and scalable code
Ability to formalize and decompose the research process into structured, repeatable components suitable for automation
Motivation to shift from manual research to building and scaling agent-driven research systems
HFT Researcher
⚲ Netherlands / Remote
About the Role
As an HFT Researcher, you will join our research team and shift the paradigm of short-horizon systematic trading — from manual strategy research to scalable, agent-driven research systems. Instead of directly building trading strategies, you will focus on encoding your expertise into autonomous agents that generate, test, and optimize strategies at scale. You will operate at the intersection of market microstructure, modeling, and system design, transforming research into a continuously learning pipeline.
Formalize the research process for short-horizon trading by defining hypothesis spaces, validation logic, and the full lifecycle from idea to evaluation
Design and build agent-driven research systems that autonomously generate, test, and optimize trading strategies
Translate market microstructure intuition into machine-executable features, signals, and constraints that guide agent behavior
Develop and adapt backtesting and simulation frameworks to support large-scale, autonomous experimentation under realistic execution conditions
Optimize research scalability by increasing throughput of hypothesis generation, balancing exploration vs exploitation, and ensuring statistical robustness
Collaborate with engineering to integrate agent-based research systems into production trading pipelines and continuously improve their performance
You’ll work at the frontier of real-world ML, with freedom to define problems, test ideas, and push them into live trading systems.
About the Team
We are building a large, proprietary hedge fund where technology is the core advantage. The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI. The fund operates tightly with an internal AI Research Lab; together we design and own the full stack: from research ideas to production trading systems.
You might thrive in this role if you have
1+ year of experience in HFT or short-horizon systematic trading, with a clear understanding of how strategies are researched, validated, and deployed
Strong understanding of market microstructure, exchange mechanics, and execution constraints
Solid grounding in probability, statistics, and optimization, with the ability to apply them in noisy, real-world settings
Strong programming skills in Python and ML frameworks, with the ability to write efficient, clean, and scalable code
Ability to formalize and decompose the research process into structured, repeatable components suitable for automation
Motivation to shift from manual research to building and scaling agent-driven research systems
