AI/ML Engineer

• 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
• 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

Benefits

• Physical: We believe in taking care of your physical health, so as a Chimer you’ll get medical, dental, and vision insurance, as well as benefits to support your physical activity, family planning, and all of your little Chimers
• Emotional: We know that staying healthy involves a lot more than doctor’s checkups. We partner with Modern Health to help you navigate your mental health, give monthly Take Care of Yourself Days, and want you to take time off when you need it
• Financial: We want Chimers to save and feel financially supported, so we offer savings plans with competitive matching, as well as access to financial planning and legal assistance so that you can find your financial peace of mind
• Social: We’ll celebrate your milestones together with travel stipends, swag, and high-fives and make sure you’re connected to fellow Chimers through our Chime Resource Groups (CRGs) and company events

• The ability to operate independently in ambiguous environments, and to communicate clearly with both technical and non-technical partners
• Strong proficiency in Python and SQL, with deep learning frameworks such as PyTorch and distributed compute such as Spark or PySpark
• 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
• 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
• 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
• Trust & Safety — fraud, risk, abuse detection, or adversarial modeling
• Lending — credit or underwriting models, and familiarity with model governance, explainability, and fairness requirements
• Growth — personalization, recommendation, marketing measurement, or experimentation at scale
• Foundation Models — transformers on tabular or semi-structured data, large-scale pretraining, ML platform work, or applied research with a publication record