About this role
About the role
Chime's Data Science & Machine Learning team builds the models, services, and platforms behind how millions of members manage and grow their financial lives. We're hiring AI/ML Engineers across several teams, Trust & Safety, Lending, Growth, and Foundation Models, and you'll be matched with the team where your background, experience and interests fit best.
In this role, you'll build and deploy machine learning systems on some of the richest transactional and behavioral data in fintech, turning it into decisions that protect members from fraud, expand access to credit, and power the personalized experiences and marketing that help millions of members get more out of products like MyPay, Instant Loans, and SpotMe — along with the foundational models the rest of our teams build on. This is a highly applied role: you'll own problems end to end, from framing the question through to a model running in production and moving a metric that matters.
In this role, you can expect to
• Build, train, and deploy deep learning and classical ML models on large-scale financial, transactional, and behavioral datasets
• 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
• Design and improve the systems around the model: feature pipelines, batch and real-time inference, monitoring, and retraining
• Partner with Product, Engineering, Analytics, and Risk to turn ambiguous business problems into ML solutions, and to make sure the solution is the right one
• Connect model performance to member outcomes and business metrics, and use experimentation to prove impact
• Contribute to the shared ML platform, tooling, and standards that the rest of the team builds on
• Help identify where AI/ML creates measurable impact for members — and where a simpler answer is the better one
To thrive in this role, you have
• Experience building and deploying deep learning models in production, with a solid grasp of architecture choice, training dynamics, and evaluation — and the judgment to model design choices
• Solid machine learning fundamentals: classical modeling, evaluation design, and knowing which metric actually answers the question in front of you
• Hands-on experience across the end-to-end ML lifecycle — training, experimentation, optimization, deployment, and monitoring
• Comfort with messy real-world data, including label definition, leakage, class imbalance, and train/serve skew
• Strong proficiency in Python and SQL, with deep learning frameworks such as PyTorch and distributed compute such as Spark or PySpark
• 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
• The ability to operate independently in ambiguous environments, and to communicate clearly with both technical and non-tec