Machine Learning Researcher

Role overview Develop machine-learning approaches for making sequential decisions under uncertainty, with a focus on extracting useful signals from large, noisy datasets. The work spans research and implementation: shaping hypotheses, building models and carrying promising ideas through to production.

Responsibilities - Design, train and deploy deep-learning models for production use. - Build reinforcement-learning systems for complex, partially observable environments. - Investigate how language models and agent-based methods can support decision-making. - Model time series and data whose patterns change over time. - Develop experiments to test hypotheses and measure whether proposed methods work. - Apply large-scale optimization to unstructured data and sequential decision problems.

Requirements - PhD in computer science, statistics or a related computational discipline, with published AI research. - At least five years working with production systems. - Experience taking deep-learning models from design through deployment. - Background in reinforcement learning for high-dimensional, partially observable settings. - Working knowledge of game theory and multi-agent systems. - Experience with time series, non-stationary data, distributed optimization and noisy, unstructured data.

Nice to have - Experience in sequential decision-making fields such as robotics, control, recommendation systems or games. - Familiarity with financial markets; prior market experience is welcome but not required. - Hands-on work with large language models and agentic systems.

Benefits and work setup - Full-time, remote role.