Staff Full-Stack Software Engineer, AI & App Experience

About the Role Chime is looking for a Staff Software Engineer to help shape the future of member-facing AI. You’ll set the technical direction for how we build with LLMs, architecting the agent systems, evaluation frameworks, and guardrails that enable us to deliver AI experiences that are reliable, safe, and scalable to millions of members. You’ll stay deeply hands-on, designing new agent capabilities and conversational experiences, rapidly prototyping and shipping production code, and raising the technical bar across the team. You’ll bring strong engineering fundamentals and a product mindset, owning capabilities from problem definition through measurement and iteration. You’ll join the AI & App Experience (AAX) organization and help build Jade, Chime’s AI-powered financial assistant. AAX is creating the next generation of the Chime member experience, with Jade at the center, helping millions of members better understand their finances, manage spending and subscriptions, and make progress toward their financial goals through experiences that feel natural, useful, and trustworthy. The AI landscape evolves rapidly, so does Chime. You'll need to learn quickly, adapt to shifting priorities, and proactively identify what's needed next, whether that's a new financial capability, a better way to evaluate conversations, or a system to keep quality high as we scale. If you're excited by the unique challenges of building LLM-powered products at scale, this is the role for you. The base salary offered for this role and level of experience will begin at $223,000.00 and up to $308,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience. In this role, you can expect to Set the technical direction and architecture for how Chime builds with LLMs on Jade: the agent architectures, prompt strategies, and orchestration patterns that shape how Jade reasons and acts, and that other engineers build on Design, build, and scale new member-facing capabilities for Jade, from prototype through production, moving fluidly between product discovery and hands-on engineering Build the eval frameworks, observability, and guardrail systems that let the team ship LLM-powered features with speed, safety, and confidence Develop and harden the backend services and internal tooling behind Jade (model routing, prompt management, agent orchestration, and evaluation pipelines), improving reliability and performance as we scale Leverage AI and LLMs natively in your own workflow, using AI-assisted coding and rapid prototyping, and turn one-off AI workflows into reusable systems (agent loops, evals, custom tooling) that compound the whole team's output Champion AI-native development practices across the team: set the quality gates that keep AI-assisted output production-ready, encode recurring failure modes into shared evals and guardrails, and push the team to work at the frontier of what AI tooling makes possible Exercise judgment about where and how AI is applied, deciding which problems get an AI-generated first pass and which need human judgment, and calibrating model autonomy to the stakes and reversibility of each decision Drive experimentation and rapid iteration: design A/B tests, analyze results, and make data-informed decisions about what to scale, pivot, or kill Partner cross-functionally with product, design, data science, and risk to understand member pain points and deliver secure, scalable solutions Contribute to technical design and uphold high standards across the codebase through code reviews and mentorship, multiplying the impact of the engineers around you Participate in on-call rotation; being on call may include responding to incidents outside of regular working hours when necessary To thrive in this role, you have 8+ years of backend or full-stack software development experience in production environments Deep expertise in system design, distributed systems, and architectural patterns for high-scale systems Proficiency with Python or comparable frameworks, with the breadth to make sound decisions across the stack AI-native fluency: you actively build with LLMs, AI code assistants, and generative AI tooling as a daily part of your workflow, not as a side project Hands-on experience building or shipping LLM-powered product features (agents, conversation experiences, evals, prompt strategies, or guardrails) at production scale A track record of turning AI into durable leverage: reusing and improving workflows, encoding recurring fixes into evals, rules, and tooling instead of solving the same problem twice, and acting as the first and most critical reviewer of AI-generated output Sound judgment about where and how to apply AI, calibrating verification effort and model autonomy to the stakes, reversibility, and cost of each task Exp