Founding Machine Learning Engineer
On-site · San Francisco, California, United States
Job Summary
Founding MLE on the Risk Engineering & Capital Products team responsible for end-to-end credit risk modeling—from problem framing and data pipelines to model development, validation, production serving, and monitoring. Collaborate with engineering, product, capital markets, and external partners to shape modeling approaches, select appropriate techniques (gradient-boosted trees, deep models, LLMs, or classical statistics), design data pipelines, deploy into production, monitor drift, and iterate. You’ll define hiring bar for the data science team, own data paths, and help scale the model-driven risk scoring framework across the wholesale-finance platform. Strong emphasis on production-grade modeling, cross-functional communication, and hands-on leadership in building and operating ML systems.
Required Qualifications
- 5+ years of experience building models for production use cases
- Strong command of supervised learning on tabular data, feature engineering, model selection, hyperparameter tuning, calibration, offline and online evaluation
- Data engineering skills — ETL and feature pipelines end-to-end
- Proficiency in Python and ML ecosystem (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost/LightGBM)
- Strong statistical reasoning — leakage, bias, label noise, spurious correlations, offline vs online metric gaps
- Track record of owning models end-to-end from problem statement to production system
- Excellent written and spoken English communication; ability to explain model behavior to non-ML audiences
- Team-oriented mindset and ability to foster a healthy, supportive work environment
- Bonus: credit risk modeling (PD/LGD/EAD), SMB or subprime underwriting; fintech or regulated modeling environments; ML platform components; production services; data analysis prowess; experience with LLMs; first/founding ML hire experience; advanced degree; BS/MS in related fields; equal employment opportunity statements
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