Architect of Autonomous Research Platform
⚲ Canada / Netherlands / Remote
About the Role
As an Architect of Autonomous Research Platform, you will lead the design and development of a self-improving system that discovers, evaluates, and deploys trading strategies with minimal human intervention.
You will operate at the intersection of AI for science, automated discovery systems, and quantitative trading, building the infrastructure that replaces manual research with scalable, agent-driven exploration.
You will own key parts of the research-to-production system, including:
Design and build a multi-agent research pipeline that generates hypotheses about market mechanisms, synthesizes strategies, and iterates through large-scale experimentation loops
Define and implement a Strategy DSL and compiler, enabling safe, structured representation of strategies across research, simulation, and live trading
Develop a falsification-first evaluation framework to eliminate false alpha (overfitting, leakage, non-robust signals) before deployment
Create an alpha memory system to track hypotheses, experiments, and outcomes, preventing repeated exploration of invalid ideas
Define and implement evaluation metrics beyond backtest PnL, including robustness, regime stability, capacity, execution realism, and novelty
Work closely with engineering to ensure seamless transition from research artifacts to experiment infrastructure, simulation and production trading systems, including monitoring, feedback, and continuous adaptation
About the Team
We are building a proprietary hedge fund where the core advantage is not individual strategies, but the system that discovers them. Our goal is to rethink systematic trading by combining: autonomous research agents, large-scale experimentation, realistic simulation and execution, modern AI and learning systems
The fund operates in close integration with an internal AI research lab. Together, we design and own the full stack — from hypothesis generation to live trading.
You might thrive in this role if you have
Experience in AI for science, AutoML, program synthesis, or automated discovery systems
Solid grounding in probability, statistics, and experimental design, especially in non-stationary environments
Experience building or working with agent-based systems, iterative pipelines, or large-scale experimentation frameworks
Ability to design systems, not just models, including abstractions such as DSLs, evaluation frameworks, and research pipelines
Programming skills in Python and ML frameworks, with a focus on writing clear, efficient, and production-ready code
Practical experience in quantitative trading (HFT, MFT, or systematic strategies), including understanding of real-world constraints
Understanding of market microstructure, execution, and why strategies fail in production
Architect of Autonomous Research Platform
⚲ Canada / Netherlands / Remote
About the Role
As an Architect of Autonomous Research Platform, you will lead the design and development of a self-improving system that discovers, evaluates, and deploys trading strategies with minimal human intervention.
You will operate at the intersection of AI for science, automated discovery systems, and quantitative trading, building the infrastructure that replaces manual research with scalable, agent-driven exploration.
You will own key parts of the research-to-production system, including:
Design and build a multi-agent research pipeline that generates hypotheses about market mechanisms, synthesizes strategies, and iterates through large-scale experimentation loops
Define and implement a Strategy DSL and compiler, enabling safe, structured representation of strategies across research, simulation, and live trading
Develop a falsification-first evaluation framework to eliminate false alpha (overfitting, leakage, non-robust signals) before deployment
Create an alpha memory system to track hypotheses, experiments, and outcomes, preventing repeated exploration of invalid ideas
Define and implement evaluation metrics beyond backtest PnL, including robustness, regime stability, capacity, execution realism, and novelty
Work closely with engineering to ensure seamless transition from research artifacts to experiment infrastructure, simulation and production trading systems, including monitoring, feedback, and continuous adaptation
About the Team
We are building a proprietary hedge fund where the core advantage is not individual strategies, but the system that discovers them. Our goal is to rethink systematic trading by combining: autonomous research agents, large-scale experimentation, realistic simulation and execution, modern AI and learning systems
The fund operates in close integration with an internal AI research lab. Together, we design and own the full stack — from hypothesis generation to live trading.
You might thrive in this role if you have
Experience in AI for science, AutoML, program synthesis, or automated discovery systems
Solid grounding in probability, statistics, and experimental design, especially in non-stationary environments
Experience building or working with agent-based systems, iterative pipelines, or large-scale experimentation frameworks
Ability to design systems, not just models, including abstractions such as DSLs, evaluation frameworks, and research pipelines
Programming skills in Python and ML frameworks, with a focus on writing clear, efficient, and production-ready code
Practical experience in quantitative trading (HFT, MFT, or systematic strategies), including understanding of real-world constraints
Understanding of market microstructure, execution, and why strategies fail in production
Architect of Autonomous Research Platform
⚲ Canada / Netherlands / Remote
About the Role
As an Architect of Autonomous Research Platform, you will lead the design and development of a self-improving system that discovers, evaluates, and deploys trading strategies with minimal human intervention.
You will operate at the intersection of AI for science, automated discovery systems, and quantitative trading, building the infrastructure that replaces manual research with scalable, agent-driven exploration.
You will own key parts of the research-to-production system, including:
Design and build a multi-agent research pipeline that generates hypotheses about market mechanisms, synthesizes strategies, and iterates through large-scale experimentation loops
Define and implement a Strategy DSL and compiler, enabling safe, structured representation of strategies across research, simulation, and live trading
Develop a falsification-first evaluation framework to eliminate false alpha (overfitting, leakage, non-robust signals) before deployment
Create an alpha memory system to track hypotheses, experiments, and outcomes, preventing repeated exploration of invalid ideas
Define and implement evaluation metrics beyond backtest PnL, including robustness, regime stability, capacity, execution realism, and novelty
Work closely with engineering to ensure seamless transition from research artifacts to experiment infrastructure, simulation and production trading systems, including monitoring, feedback, and continuous adaptation
About the Team
We are building a proprietary hedge fund where the core advantage is not individual strategies, but the system that discovers them. Our goal is to rethink systematic trading by combining: autonomous research agents, large-scale experimentation, realistic simulation and execution, modern AI and learning systems
The fund operates in close integration with an internal AI research lab. Together, we design and own the full stack — from hypothesis generation to live trading.
You might thrive in this role if you have
Experience in AI for science, AutoML, program synthesis, or automated discovery systems
Solid grounding in probability, statistics, and experimental design, especially in non-stationary environments
Experience building or working with agent-based systems, iterative pipelines, or large-scale experimentation frameworks
Ability to design systems, not just models, including abstractions such as DSLs, evaluation frameworks, and research pipelines
Programming skills in Python and ML frameworks, with a focus on writing clear, efficient, and production-ready code
Practical experience in quantitative trading (HFT, MFT, or systematic strategies), including understanding of real-world constraints
Understanding of market microstructure, execution, and why strategies fail in production
