AI/ML Engineer

Responsibilities

• Contribute to the design and implementation of training pipeline components for AI/ML models that support Chime’s risk decisioning systems.
• Develop, test, and iterate on model features within clear requirements and with support from senior team members.
• Support offline model evaluation and contribute to online experiment analysis to understand performance, tradeoffs, and member impact.
• Write modular, testable, and maintainable code following engineering best practices.
• Collaborate with Product Managers, Engineers, and Risk teams to translate model findings into clear recommendations and measurable member impact.
• Contribute to production-facing model workflows, including model training, tuning, inference, and monitoring.
• Contribute to projects that apply modern AI/ML methods, such as generative AI, sequence models, and automation workflows, to improve Risk decisioning.

Requirements

• 1–2 years of experience in applied data science or AI/ML engineering, including relevant internship, academic, or project experience.
• Working knowledge of machine learning fundamentals, including feature development, model training, validation, tuning, and evaluation.
• Familiarity with cloud platforms, preferably AWS, orchestration tools, and version control.
• Familiarity with offline model evaluation, experimentation, and model performance tradeoffs.
• Ability to communicate analyses and model findings clearly, clarify requirements, and collaborate effectively with technical and non-technical partners.

Nice to Have

• Exposure to production ML workflows, including inference, monitoring, retraining, orchestration, model deployment.
• Experience using AI-assisted development tools such as Cursor, Claude Code, or similar tools.
• Exposure to deep learning methods, such as embeddings, sequence models, representation learning, or behavioral modeling.