Senior ML Engineer (Regulatory)

- Passionate about leveraging cutting-edge Artificial Intelligence and Machine Learning to ensure the integrity and transparency of global financial markets - Data-reasoning instinct — able to say what the data is telling you and what data should go into a model in the first place, not just which model to reach for - Strong SQL and experience with large-scale datasets - Production ML experience with time-series / sequential data — you've trained, deployed, and monitored models at scale, and you understand how time affects the structure of data: stationarity, regime change, leakage, and why a model that looks good in backtest fails live - Deep learning applied to temporal or representation problems — sequence models, embeddings/similarity over time-series, or equivalent - Excellent written and verbal communication - Solid software-engineering foundation: 5+ years, primarily Python, with production practices (version control, automated testing, CI/CD, Docker) and comfort in an enterprise cloud data platform (Snowflake / Databricks / BigQuery, etc.) under real RBAC and governance constraints - Bachelor's degree in a quantitative field - We work across deep learning, LLM agent systems, and classical ML. While you don't need to know all of these, you should have real depth in at least a couple of these, and curiosity about the rest: - Deep learning: PyTorch, custom training loops, architecture design and experimentation, multi-GPU distributed ML, experiment tracking, model…