Staff AI/ML Engineer

Join Chime, a leading fintech company, as a Staff AI/ML Engineer on our Lending and Cashflow team. In this role, you will lead the modeling behind cash flow underwriting and surface member insights. You will build and deploy machine learning systems using members' transaction and deposit data to make fair and accurate credit decisions. You will own problems end to end, set the technical direction for cash flow data underwriting, and connect model performance to member outcomes and business metrics.

- Lead the design, training, and deployment of deep learning and classical ML underwriting models that turn members' transaction and cash flow data into credit decisions. - Take models from problem framing through to production — training, evaluation, deployment, monitoring, and iteration — and stay accountable for how they behave once they're live. - Set the technical roadmap for Lending ML and raise the bar across the team through design reviews, mentorship, and shared standards for model development, validation, and governance.

- Strong proficiency in Python and SQL, with deep learning frameworks such as PyTorch and distributed compute such as Spark or PySpark - Comfort with messy real-world data, including label definition, leakage, class imbalance, and train/serve skew - Hands-on experience across the end-to-end ML lifecycle — training, experimentation, optimization, deployment, and monitoring - The ability to operate independently in ambiguous environments, influence technical direction beyond your own team, and explain model decisions clearly to engineers, risk partners, and senior leaders - Working knowledge of modern ML infrastructure — AWS and tools such as SageMaker, Airflow, Kafka, Redis, and Snowflake — and an MLOps mindset for keeping production systems healthy - A track record of leading the design and deployment of deep learning models in production, including multi-quarter initiatives that span teams, with a solid grasp of architecture choice, training dynamics, and evaluation, and the judgment to make model design choices - Solid machine learning fundamentals: classical modeling, evaluation design, and knowing which metric actually answers the question in front of you - Experience in either of these is a plus: - Credit or underwriting models, and familiarity with model governance, explainability, and fairness requirements, such as adverse action reason codes and fair lending testing - Cash flow or alternative-data underwriting: building credit features from bank transaction data, such as income and expense detection, balance trends, and transaction categorization - Deployed transformer models in production