Ai/ml Engineer

Chime Chime · Fintech · San Francisco, CA · Data Science & Machine Learning

AI/ML Engineer on the Trust & Safety team at Chime, focusing on building and deploying models for risk detection, member behavior understanding, and decisioning systems. The role involves end-to-end model development, including training pipelines, feature development, offline evaluation, online experiment analysis, and contributing to production workflows like tuning and inference. It emphasizes applying modern AI/ML methods such as generative AI and sequence models to financial risk challenges in a high-scale environment.

What you'd actually do

  1. Contribute to the design and implementation of training pipeline components for AI/ML models that support Chime’s risk decisioning systems.
  2. Develop, test, and iterate on model features within clear requirements and with support from senior team members.
  3. Support offline model evaluation and contribute to online experiment analysis to understand performance, tradeoffs, and member impact.
  4. Write modular, testable, and maintainable code following engineering best practices.
  5. Collaborate with Product Managers, Engineers, and Risk teams to translate model findings into clear recommendations and measurable member impact.

Skills

Required

  • 1–2 years of experience in applied data science or AI/ML engineering
  • machine learning fundamentals
  • feature development
  • model training
  • validation
  • tuning
  • evaluation
  • cloud platforms (AWS preferred)
  • orchestration tools
  • version control
  • offline model evaluation
  • experimentation
  • model performance tradeoffs
  • communication
  • collaboration

Nice to have

  • production ML workflows
  • inference
  • monitoring
  • retraining
  • orchestration
  • model deployment
  • AI-assisted development tools
  • deep learning methods
  • embeddings
  • sequence models
  • representation learning
  • behavioral modeling

What the JD emphasized

  • end-to-end model development
  • production-facing decision systems
  • risk challenges
  • applied ML
  • generative AI
  • sequence models
  • production ML workflows

Other signals

  • building models
  • decisioning systems
  • risk challenges
  • applied ML
  • generative AI
  • sequence models