AI/ML Engineer

About the Role

Chime's Data Science & Machine Learning team builds models, services, and platforms that help members manage and grow their financial lives. AI/ML Engineers are being hired across Trust & Safety, Lending, Growth, and Foundation Models, with team matching based on background, experience, and interests.

This applied role owns machine learning problems end to end, from framing questions to deploying production models and measuring impact. Models support fraud prevention, access to credit, personalized experiences, marketing, and foundational capabilities used by other teams.

Responsibilities

• Build, train, and deploy deep learning and classical machine learning models on large-scale financial, transactional, and behavioral datasets.
• Own training, evaluation, deployment, monitoring, and iteration, remaining accountable for production behavior.
• 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 ML solutions.
• Connect model performance to member outcomes and business metrics, using experimentation to demonstrate impact.
• Contribute to shared ML platforms, tooling, and standards.
• Identify where AI/ML provides measurable value and where simpler approaches are preferable.

Qualifications

• Experience building and deploying production deep learning models, including architecture selection, training dynamics, and evaluation.
• Solid classical machine learning fundamentals, evaluation design, and metric selection.
• Hands-on experience across training, experimentation, optimization, deployment, and monitoring.
• Comfort with messy real-world data, including label definition, leakage, class imbalance, and train/serve skew.
• Strong Python and SQL proficiency, 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, plus an MLOps mindset.
• Ability to operate independently in ambiguous environments and communicate clearly with technical and non-technical partners.

Preferred Experience

Experience in any of these areas is a plus and helps determine team placement:

• Trust & Safety: Fraud, risk, abuse detection, or adversarial modeling.
• Lending: Credit or underwriting models, 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 publications.

Location and Work Policy

• Locations: Chicago, IL; New York, NY; San Francisco, CA.
• The posting is tagged onsite. The stated office policy is four days per week in the office and Fridays from home for employees near an office; the company also mentions a fully remote program without specifying eligibility for this role.

Compensation and Benefits

• Listed annual base salary: $125,000–$292,000 USD. Actual base salary may be higher depending on location, skills, qualifications, and experience.
• Full-time employees may be eligible for bonuses, competitive equity, and benefits.
• Benefits for regular full-time employees include comprehensive health, financial, and wellbeing benefits; backup child, elder, and pet care; eligible commuter subsidies; generous vacation and company-wide paid days off; community service time; an annual wellness stipend; family planning reimbursement; and up to 22 weeks of paid parental leave for birthing parents and 12 weeks for non-birthing parents.

Application Accommodations

Applicants needing accommodations during the application process may contact accommodations@chime.com.