Machine Learning Researcher - Research & Development

EngineeringFull-timeOn-site London# Machine Learning Researcher - Research & DevelopmentAnnual base salary: £150,000 - £180,000 GBP1. 2. 3. Machine Learning Researcher - Research & Development Role overviewWestren Capital is a London-based proprietary trading firm specialising in digital asset markets. We are committed to rigorous, first-principles research at the intersection of quantitative finance and machine learning, bringing together talent from Mathematics, Physics, and Computer Science to translate cutting-edge research into actionable signals across global markets.Our culture is built around intellectual honesty, independence of thought, and a deep respect for evidence over narrative. Collaboration is not ornamental here, it is structural - the best ideas tend to emerge where disciplines overlap and perspectives collide.This role sits within our AI team, a focused R&D group of quantitative researchers, engineers, and ML practitioners working on frontier problems in representation learning and large-scale modelling applied to markets. Responsibilities - Design, train, and evaluate machine learning models aimed at extracting signal from unstructured and structured market data - Work closely with traders and quant researchers to understand real constraints, not idealised ones, before building anything - Take models from notebook to production, which means caring about latency, robustness, and failure modes as much as accuracy Stay current with the ML research landscape and bring back what's actually useful, filtering out what's only impressive - Contribute to shared infrastructure and research workflows that make the next project faster than this one Requirements - Strong grounding in ML fundamentals, with practical experience in modern architectures such as transformers or related approaches - Comfortable writing production-quality code in Python and/or C++, familiar with a major framework such as PyTorch, TensorFlow, or JAX - Evidence of taking a model from idea to something that worked in a real system, not just a benchmark leaderboard - Curiosity and range matter more than narrow specialism; a working sense of markets, or a willingness to build one fast, is expected - Experience with distributed training, HPC environments, or GPU-level optimisation (CUDA/ROCm) is a strong plus - Comfortable operating with judgement in ambiguous territory, and willing to let that judgement bend when the evidence says otherwise