AUTONOMOUS-FOX
AUTONOMOUS-FOX
LABORATORIES
About us
What we do....
Autonomous-Fox Laboratories is an independent research lab in London developing fundamental machine-learning methods for financial markets.
Our research focuses on learning representations of complex systems: how behaviour develops through time, how relationships form across a system and how those relationships change. Financial markets bring these challenges together in a particularly demanding setting. Observations are abundant, but the structure behind them is obscured by noise and continual change.
We bring together fundamental AI research and more than twelve years of experience developing machine-learning models for financial markets. Our ambition is to make a deeper understanding of market structure available to the systems that act on it.
Some are bulls, others bears. We are foxes. We craft intelligent systems to understand markets across all conditions.
Advancing the next wave of AI: learning representations of complex systems beyond human intuition.
Embracing Complexity
Financial markets are complex systems. Their behaviour emerges from interactions among participants, assets and institutions, across different timescales. Relationships overlap, change with conditions and connect individual activity to movements across the wider market. Understanding that organisation requires more than studying assets in isolation or measuring correlations between pairs. Our research investigates how AI can learn the structure of these interactions, including relationships within and between groups, and how that structure evolves.
The Next Wave of AI
Language and vision have driven remarkable progress in AI, largely in capabilities humans already possess. We believe the next wave will increasingly address complex systems whose behaviour is beyond our capacity to reason through. Financial markets belong firmly within this frontier. Advancing AI here requires more than applying models developed for other domains. It demands fundamental research into what models learn, how they represent uncertainty and how they account for a changing environment. These questions are central to our work at Autonomous-Fox.
Learning the Structure of Markets
Similar market movements can arise under different conditions, while related behaviour can appear very different across assets and timescales. Learning market structure means recognising what those observations have in common without losing the differences that matter. We investigate how models can learn abstractions that preserve useful information, suppress noise and retain the context needed to interpret behaviour. The aim is a representation through which recurring patterns and their relationships become easier to learn, compare and use.
Fundamental Research
Progress in financial AI depends on what models can learn from observations that are noisy, incomplete and continually changing. The challenge reaches into the foundations of machine learning: what to represent, which distinctions to preserve and how to learn relationships that change with context. We develop original methods to address these questions. Our work combines representation learning, temporal modelling and relational structure learning, with theory and experiment guiding the development of new approaches.
A Model for All Markets
Our ambition is a general-purpose model of financial markets, built through frontier AI research into the structure of complex systems.
Its scope extends beyond any single trading objective. Patterns develop through time, relationships form across assets and groups, and their significance changes with context. We are developing methods to bring that wider structure into a common representation, giving individual strategies access to information they would otherwise need to learn for themselves.
Each strategy would retain its own signals and decisions while drawing on the model’s broader market context. The same research could inform execution, portfolio construction, risk management and market making.
The ambition is larger than any one application: a proprietary scientific asset whose advances can strengthen an entire family of trading systems.
Experience and Expertise
16+
years modelling financial markets
12+
Years of Experience in Machine Learning
3
interconnected research streams
>80%
of capital directed to R&D
20+
First- or joint-first-author papers at top-tier AI conferences
Join the Team
We are recruiting researchers to help shape a frontier AI research programme in financial markets. Our focus is learning representations of complex systems: how their behaviour develops through time and how interactions give rise to structure across the system.
Opportunities include senior research scientist roles, co-founder-level appointments and positions for early-stage researchers completing or holding a doctorate. We welcome expertise in representation learning, temporal and sequence modelling, relational structure learning, geometric and Topological deep learning, probabilistic modelling and related fields.
We seek scientific independence, mathematical rigour and the ability to develop new ideas through theory and experiment. Joining a small team at this stage offers substantial influence over the scientific direction and the methods we develop.
If your background is in machine learning, computer science, mathematics, statistical physics or a related discipline, and you want to advance fundamental AI research on complex systems, we would like to hear from you.