Staff AI/ML Engineer

Chime Financial • San Francisco, CA

12h • $246,000 - $340,000

About The Position

Chime’s Data Science & Machine Learning team builds models, services, and platforms that help members manage and grow their financial lives. The Staff AI/ML Engineer will join the Lending and Cashflow team to lead modeling for cash flow underwriting and member insights, using transaction and deposit data to support fair, accurate credit decisions. The work supports products including MyPay, Instant Loans, and SpotMe.

This is an applied, end-to-end role: the engineer will frame problems, build and deploy production models, and remain accountable for their performance and impact. The role also sets technical direction for cash-flow underwriting and Lending ML, including systems, standards, and governance. The stated base salary range is $246,000–$340,000 USD. Full-time employees may also be eligible for bonuses, equity, and benefits; actual base salary may vary based on location, skills, qualifications, and experience.

Requirements

• Track record leading the design and production deployment of deep learning models, including multi-quarter initiatives spanning teams.
• Strong understanding of model architecture choices, training dynamics, evaluation, and model-design judgment.
• Solid machine-learning fundamentals, including classical modeling, evaluation design, and choosing appropriate metrics.
• Hands-on experience across the end-to-end machine-learning lifecycle, including training, experimentation, optimization, deployment, and monitoring.
• Experience working with messy real-world data, including label definition, data leakage, class imbalance, and train/serve skew.
• Strong proficiency in Python and SQL.
• Experience with deep-learning frameworks such as PyTorch and distributed compute such as Spark or PySpark.
• Working knowledge of modern ML infrastructure, including AWS and tools such as SageMaker, Airflow, Kafka, Redis, and Snowflake.
• An MLOps mindset for maintaining healthy production systems.
• Ability to work independently in ambiguous environments, influence technical direction beyond one’s own team, and clearly explain model decisions to engineers, risk partners, and senior leaders.

Nice To Haves

• Experience with credit or underwriting models and familiarity with model governance, explainability, and fairness requirements, such as adverse action reason codes and fair-lending testing.
• Experience building cash-flow or alternative-data underwriting features from bank transaction data, such as income and expense detection, balance trends, and transaction categorization.
• Experience deploying transformer models in production.

Responsibilities

• Lead the design, training, and deployment of deep learning and classical machine-learning underwriting models using transaction and cash-flow data for credit decisions, including approvals, line assignments, and loss forecasting.
• Own the model lifecycle from problem framing through training, evaluation, deployment, monitoring, and iteration, and remain accountable for live model behavior.
• Design and improve feature pipelines, batch and real-time inference, monitoring, and retraining systems.
• Partner with Product, Engineering, Analytics, and Risk to translate ambiguous business problems into appropriate machine-learning solutions.
• Connect model performance to member outcomes and business metrics, using experimentation to demonstrate impact.
• Set the technical roadmap for Lending ML and raise team standards through design reviews, mentorship, and shared practices for model development, validation, and governance.
• Identify opportunities where AI/ML can measurably benefit members, and determine when a simpler solution is more appropriate.

Benefits

• Eligible bonuses and competitive equity for full-time employees.
• Backup child, elder, and pet care.
• Subsidized commuter benefits for eligible employees.
• Comprehensive health, financial, and wellbeing benefits.
• Generous vacation policy and company-wide paid days off.
• Paid time to support local community organizations.
• Annual wellness stipend for eligible wellness expenses.
• Up to 22 weeks of paid parental leave for birthing parents and 12 weeks for non-birthing parents.
• Family planning reimbursement.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed