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Salary: USD 246,000 - 340,000 / annual
Chime's Data Science & Machine Learning team is hiring a Staff AI/ML Engineer to lead machine learning systems for lending and cashflow underwriting. You'll build and deploy models on transactional and behavioral data to make fair, accurate credit decisions for products like MyPay, Instant Loans, and SpotMe.
In this role, you'll own problems end-to-end: framing questions, training and deploying models, monitoring production performance, and iterating based on real-world outcomes. You'll set the technical direction for how Chime underwrites using cashflow data, designing systems around models including feature pipelines, batch and real-time inference, monitoring, and retraining.
Key responsibilities include:
- Lead design, training, and deployment of deep learning and classical ML underwriting models that turn transaction and cashflow data into credit decisions (approval, line assignment, loss forecasting)
- Take models from problem framing through production—training, evaluation, deployment, monitoring, iteration—and stay accountable for live behavior
- Design and improve systems around models: feature pipelines, batch/real-time inference, monitoring, retraining
- Partner with Product, Engineering, Analytics, and Risk to turn business problems into ML solutions
- Connect model performance to member outcomes and business metrics using experimentation
- Set technical roadmap for Lending ML and raise team standards through design reviews, mentorship, and governance
- Identify where AI/ML creates measurable impact versus simpler solutions
Requirements:
- Track record leading design and deployment of deep learning models in production, including multi-quarter cross-team initiatives, with solid grasp of architecture, training dynamics, and evaluation
- Solid ML fundamentals: classical modeling, evaluation design, metric selection
- Hands-on experience across end-to-end ML lifecycle: training, experimentation, optimization, deployment, monitoring
- Comfort with messy real-world data: label definition, leakage, class imbalance, train/serve skew
- Strong proficiency in Python and SQL; deep learning frameworks (PyTorch); distributed compute (Spark/PySpark)
- Working knowledge of modern ML infrastructure: AWS, SageMaker, Airflow, Kafka, Redis, Snowflake; MLOps mindset
- Ability to operate independently in ambiguous environments, influence technical direction beyond own team, explain model decisions to engineers, risk partners, and senior leaders
Nice-to-have:
- Credit or underwriting model experience; familiarity with model governance, explainability, fairness (adverse action codes, fair lending testing)
- Cashflow or alternative-data underwriting: building credit features from bank transaction data (income/expense detection, balance trends, transaction categorization)
- Deployed transformer models in production
