Machine Learning Researcher

Role overview Push the frontier of applied AI for trading by designing models that reason and act under uncertainty, extract signal from vast noisy datasets, and own research ideas from hypothesis through production. The role combines deep learning, reinforcement learning, and large language model work with a setting that rewards curiosity about financial markets.

Responsibilities - Design, train, and ship deep learning models that operate in production trading systems. - Build reinforcement learning systems for partially observable, high-dimensional environments. - Apply large language models and agentic architectures to research and decision workflows. - Model non-stationary data, time series, and large-scale distributed optimization problems. - Run rigorous experimental design and hypothesis testing, including signal extraction from noisy unstructured data. - Translate research output into production systems that handle sequential decision-making at scale.

Requirements - PhD in computer science, statistics, or a computational field, with published research in artificial intelligence. - Demonstrated experience designing, training, and shipping deep learning models to production. - Hands-on work with reinforcement learning in partially observable, high-dimensional settings. - Working command of game theory and multi-agent settings. - Practical experience with large language models and agentic systems. - At least five years of experience working in production environments.

Nice to have - Background applying these methods in domains involving sequential decision-making such as robotics, control, recommendation, or games. - Prior experience in financial markets or quantitative trading contexts. - A deep curiosity for how financial markets behave and how models can capture that behavior.