AI/ML Engineer
125k – 173kSan Francisco, CAHybrid1+ YOE
Summary
Build and productionize ML models for risk detection and decisioning systems. Requires 1-2 years applied ML experience and familiarity with AWS, model evaluation, and experimentation.
About the role
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.
Skills
Machine LearningAWSPythonModel TrainingModel EvaluationExperimentationFeature EngineeringGenerative AISequence ModelsVersion Control
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