Senior Machine Learning Engineer

Role overview Design, build, evaluate, deploy, and operate production machine learning systems that power a financial research and market intelligence platform. The role sits at the intersection of machine learning, software engineering, financial data, and production reliability, supporting research intelligence, financial-document understanding, retrieval and ranking, market-data analysis, anomaly detection, classification, and forecasting. The position is remote within the United States, with periodic business travel for offsites, workshops, conferences, and partner meetings.

Responsibilities - Design, develop, train, evaluate, deploy, and maintain production-grade machine learning systems. - Build reliable ML pipelines spanning data preparation, feature engineering, training, validation, inference, monitoring, and retraining. - Develop models for classification, ranking, retrieval, anomaly detection, forecasting, and financial-document intelligence. - Build machine learning services and APIs integrated into research, analytics, and market-intelligence products. - Develop scalable batch and online inference workflows with appropriate latency, reliability, and cost controls. - Design rigorous model evaluation frameworks using statistically appropriate offline and production metrics, including benchmarks, regression tests, and acceptance criteria. - Build feature pipelines from financial market data, company fundamentals, filings, macroeconomic indicators, research metadata, and text data.

Requirements - Five or more years of professional experience in machine learning engineering, applied ML, ML infrastructure, or AI engineering. - Strong proficiency in Python and solid software engineering fundamentals, including clean architecture, testing, debugging, version control, API design, and production reliability. - Demonstrated experience building and operating machine learning systems in production. - Strong knowledge of supervised and unsupervised methods, plus statistics, probability, model evaluation, experimentation, and generalization. - Experience with frameworks such as PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM, or comparable tools. - Strong SQL skills and experience with large structured, semi-structured, or unstructured datasets. - Familiarity with embeddings, retrieval, ranking, search, and representation-learning systems.

Nice to have - Experience with time-series, financial market data, quantitative research workflows, or financial document analysis. - Background that combines ML depth with strong software engineering judgment and cross-functional collaboration with product and research teams.

Benefits and work setup - Annual base salary of USD $125,000 to $155,000, depending on experience, technical depth, location, and role fit. - Full-time remote work within the United States. - Periodic business travel for planning sessions, offsites, cross-functional workshops, conferences, and partner or institutional meetings, with approved expenses handled per company policy.