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Chime Financial, Inc

Software Engineer, Support Foundations

San Francisco, CA, USAmidAdded 1 month ago

About this role

Build and maintain the infrastructure powering Chime's customer support systems, including voice IVR, live chat, AI-powered self-service, and agent routing platforms. You'll modernize contact center systems, integrate LLMs for intelligent automation, and establish data foundations that scale support quality across millions of members.

What you'll do

  • Design and develop backend systems for contact center platform (voice, chat, routing)
  • Build AI-powered self-service features and conversational flows using LLMs
  • Create real-time call and chat summarization systems for agents
  • Develop agent provisioning and skill-matching systems
  • Build secure IVR and member authentication phone flows
  • Establish data pipelines and observability dashboards for routing quality and operational health

What they're looking for

  • Ruby on Rails or comparable backend framework
  • Production-scale backend development (3+ years)
  • Transactional databases and caching systems
  • API design and mobile integration
  • System monitoring and observability tooling
  • Large-scale web application architecture
  • AI/LLM integration
  • On-call incident response

Benefits

  • Competitive salary ($133,000–$184,000)
  • Performance bonus eligible
  • Equity package
  • Full-time employment benefits
  • High-impact work directly affecting member experience
  • Collaboration with product, design, and operations teams
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Chime Financial, Inc

Chime Financial, Inc builds financial products and services for millions of members, including earned wage access, instant loans, and membership features. The company is hiring full-stack engineers, backend service developers, trust and safety engineers, and ML platform engineers to build scalable systems powering member engagement, risk management, lending, and financial transactions.

View all jobs at Chime Financial, Inc

Likely interview questions

  • Describe your experience building production systems at scale and how you approached reliability and observability.
  • Tell us about a time you integrated or worked with AI/LLM features. What challenges did you encounter?
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