AI/ML Engineer

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-technical partners

Nice-to-Have

Experience in any one of these is a plus, and helps us match you to the right team.

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

#LI-DA1 #LI-Onsite

Below is the base salary offered for this role and level of experience. Full-time employees may also be eligible for bonus(es), competitive equity, and benefits. For commissioned roles, the base salary listed in this job description does not include incentive/variable pay. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

Wage Notice

$125,000—$292,000 USD