Machine Learning Researcher (Large-Scale ML / GenAI) : Quant Fund
2 days ago
Miami
We are retained by a stealth, elite quantitative investment firm building a world-class research platform at the intersection of machine learning, generative AI, and quantitative finance. Backed by exceptional capital and leadership, this start-up environment offers the freedom and resources to pursue frontier AI research with real-world deployment. We are seeking an outstanding Machine Learning Researcher to join a small, highly selective research team. Role Overview You will work at the cutting edge of modern AI—designing, training, and scaling state-of-the-art ML and GenAI systems. This is a deeply research-driven role with direct ownership of ambitious problems and access to significant compute and data. This will result in building strategies for trading Equities, Options and Crypto. Key Responsibilities • Train large-scale ML and generative AI systems end-to-end, from raw data processing through fine-tuning and evaluation, • Develop novel AI/ML architectures, including transformer-based and generative models, • Train state-of-the-art systems across supervised, unsupervised, and reinforcement learning, • Explore and curate new datasets to extract novel signal and drive performance, • Contribute to original research and long-term AI strategy alongside elite researchers Required Background • Proven experience training ML models end-to-end, from data pipelines to fine-tuning, • Strong hands-on experience across transformers, generative models, and reinforcement learning, • Publications in top-tier ML venues (e.g., NeurIPS, ICML, ICLR, JMLR, or equivalent), • Strong Python PhD/MSc or equivalent research experience from a top-tier academic institution for example (but not limited to): • MIT, Stanford, UC Berkeley, Carnegie Mellon, Princeton, Harvard, • Oxford, Cambridge, Imperial College London, UCL, • ETH Zurich, EPFL, Max Planck Institutes, University of Tübingen Why Join • Huge backers - roadmap to $1billion AUM over next 18 months, • Great bonus and equity potential, • Work in a low-bureaucracy, high-signal research culture, • Direct access to exceptional compute, data, and research autonomy, • Ability to conduct real ML research that goes into production, • Highly competitive compensation with meaningful upside