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Salary: USD 133,000 - 184,000 / annual
Chime is seeking a Software Engineer to join the AI Enablement team, contributing to internal platforms and primitives that make AI a trusted co-pilot for employees across the company. You will work on systems like Archimedes, Bosun, and easyRAG that power internal AI assistants, developer workflows, and retrieval-backed applications.
In this role, you'll build reusable platform capabilities—APIs, SDKs, templates, and shared services—that make it easier for teams to develop safe, high-quality AI-powered tools. You'll contribute to and extend internal AI platforms, helping teams develop and scale LLM-powered applications. Working alongside senior engineers, you'll prototype and productionize new AI capabilities, turning successful patterns into durable platform features.
You'll apply Chime's golden path patterns for LLM usage and help refine them as you deepen familiarity with the systems. The role involves building features that let teams launch AI-powered assistants and workflows in a standardized, observable, and governed way. You'll learn and apply evaluation, observability, and cost-management practices across AI systems—including quality, latency, safety, and spend.
Collaboration is key: you'll partner with Security, Risk, and Legal to implement guardrails for responsible AI development, and work with engineers across Chime to embed AI into internal tools, workflows, and employee experiences.
Required qualifications include production-quality software engineering experience in distributed systems or platform environments, hands-on experience with LLMs or ML services (or strong curiosity and ability to ramp quickly), solid coding skills in Python, Go, or TypeScript/JavaScript, and a platform mindset focused on reuse and building for multiple consumers. You should be familiar with or eager to learn AI-specific engineering concerns such as evaluation, prompt and version management, observability, cost control, safety, and data privacy. Clear communication skills are essential for explaining technical tradeoffs to teammates and cross-functional partners.
