Machine Learning Engineer, Clair

• This role is ideal for someone who loves crafting meaningful features out of raw data, scaling ML systems in production, and driving the technical foundations of a modern financial ML stack
• 5+ years of experience in machine learning engineering, data science, data engineering, or a related technical field
• Strong experience in feature engineering, particularly with large-scale transactional or time-series datasets
• Proficiency in Python, SQL, and modern ML frameworks (e.g., PyTorch, XGBoost, sci-kit learn)
• Experience building production ML pipelines and deploying models in cloud environments
• Strong understanding of data modeling, forecasting, and statistical analysis
• Clear and effective communication skills when working with product, engineering, and business stakeholders
• Demonstrated ability to execute end-to-end: from raw data exploration to production ML implementation
• Software engineering proficiency with a sensible adherence to good software design patterns

Desirable

• As a Machine Learning Engineer on Clair’s Data team, you will be responsible for transforming diverse financial data sources, including but not limited to Plaid transactions, into production-grade features, predictive signals, and modeling pipelines that power Clair’s credit and underwriting systems
• Your work will focus on building the core feature layer that our credit models depend on: extracting structure from messy financial data, designing robust predictive features, forecasting key user-level events (such as income timing or financial stability indicators), and ensuring that these features are efficiently deployed, monitored, and continuously improved
• In addition to hands-on modeling and feature engineering, you will contribute to the development of Clair’s Data Science platform – building tooling, automation, and infrastructure that make it easier to train, deploy, and monitor ML systems at scale
• Engineer high-quality features from financial and behavioral data to support credit modeling
• Build and maintain scalable pipelines for feature computation, income detection, and financial signal extraction
• Develop forecasting and predictive models for income timing, user stability, and financial behavior patterns
• Deploy, monitor, and optimize ML models and feature pipelines in production using AWS, Snowflake, or other tooling
• Contribute to the development of Clair’s Data Science platform, including model deployment workflows, feature stores, and monitoring systems
• Collaborate with engineering, product, and risk teams to understand their workflows and ensure data products have high availability for their use cases
• Prototype new modeling techniques and evaluate their impact on credit and underwriting performance
• Communicate findings and technical decisions clearly to both technical and non-technical stakeholders