AI Researcher (MFT)
⚲ Amsterdam / Montreal / Dubai
About the Role
As an AI Researcher, you will join our research team and shift the paradigm of algo 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 trading by defining hypothesis spaces, validation logic, and the full lifecycle from idea to evaluation
Translate market microstructure intuition into machine-executable features, signals, and constraints that guide agent behavior
Generate novel alpha hypotheses, evaluate alpha decay, turnover, capacity, and execution sensitivity.
Combine individual signals into portfolio-level trading strategies.
Continuously improve existing strategies through automated experimentation.
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 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
2+ years of experience in trading with a clear understanding of how strategies are researched, validated, and deployed
Strong understanding of market microstructure, exchange mechanics, execution constraints and strategies and/or reinforcement learning approaches including offline RL, simulation-based RL, imitation learning for trading and portfolio management.
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
AI Researcher (MFT)
⚲ Amsterdam / Montreal / Dubai
About the Role
As an AI Researcher, you will join our research team and shift the paradigm of algo 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 trading by defining hypothesis spaces, validation logic, and the full lifecycle from idea to evaluation
Translate market microstructure intuition into machine-executable features, signals, and constraints that guide agent behavior
Generate novel alpha hypotheses, evaluate alpha decay, turnover, capacity, and execution sensitivity.
Combine individual signals into portfolio-level trading strategies.
Continuously improve existing strategies through automated experimentation.
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 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
2+ years of experience in trading with a clear understanding of how strategies are researched, validated, and deployed
Strong understanding of market microstructure, exchange mechanics, execution constraints and strategies and/or reinforcement learning approaches including offline RL, simulation-based RL, imitation learning for trading and portfolio management.
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
AI Researcher (MFT)
⚲ Amsterdam / Montreal / Dubai
About the Role
As an AI Researcher, you will join our research team and shift the paradigm of algo 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 trading by defining hypothesis spaces, validation logic, and the full lifecycle from idea to evaluation
Translate market microstructure intuition into machine-executable features, signals, and constraints that guide agent behavior
Generate novel alpha hypotheses, evaluate alpha decay, turnover, capacity, and execution sensitivity.
Combine individual signals into portfolio-level trading strategies.
Continuously improve existing strategies through automated experimentation.
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 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
2+ years of experience in trading with a clear understanding of how strategies are researched, validated, and deployed
Strong understanding of market microstructure, exchange mechanics, execution constraints and strategies and/or reinforcement learning approaches including offline RL, simulation-based RL, imitation learning for trading and portfolio management.
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
