Staff Engineer – Core Platform

Banking is being reimagined—and customers expect every interaction to be easy, personal, and instant.

We are building a universal banking assistant that millions of U.S. consumers can use to transact across all financial institutions and, over time, autonomously drive their financial goals. Powered by our proprietary BankGPT platform, this assistant is positioned to displace age-old legacy systems within financial institutions and own the end-to-end CX stack, unlocking a $200B opportunity and potentially replacing multiple publicly traded companies.

Ultimately, our mission is to drive financial well-being for millions of consumers.

With over two-thirds of Americans living paycheck to paycheck, 50% holding less than $500 in savings, and only 17% financially literate, we aim to put financial well-being on autopilot to help solve this problem.

About the Role

We’re looking for a Staff Engineer – Core Platform to architect, scale, and evolve the distributed systems foundation that powers Interface.ai’s next-generation AI experiences.

This is a hands-on, high-impact engineering role — you will design and build core platform components that enable real-time AI interactions, secure orchestration, and low-latency execution across millions of concurrent user sessions.

The ideal candidate is a systems thinker who thrives on solving large-scale engineering challenges in distributed, event-driven environments — someone who obsesses over performance, reliability, and elegant architecture, and who elevates the technical bar for the entire organization.

What You’ll Own

As a Staff Engineer, you will be the technical backbone for the Core Platform team — defining architecture, mentoring teams, and ensuring engineering excellence across all systems.

You’ll focus on:

Designing and scaling low-latency, fault-tolerant distributed systems serving real-time workloads.

Architecting microservices and event-driven systems that are secure, composable, and resilient under scale.

Integrating Vector Databases and Embedding Stores to support intelligent retrieval, RAG (Retrieval-Augmented Generation), and adaptive AI experiences.

Partnering with AI and Product teams to embed LLMs and inference services into the Core Platform, ensuring performance and observability.

Defining technical standards, best practices, and evolutionary architecture patterns across teams.

Driving continuous improvement in code quality, observability, and deployment reliability.

Acting as a technical mentor and multiplier — raising the bar for system design, code reviews, and debugging excellence.

What You’ll Do

Architect and Build Distributed Systems: Design microservice-based architectures that enable scalability, low latency, and fault isolation for AI-driven features.

Optimize System Performance: Own performance at the platform level — from network I/O and API design to database indexing and caching strategies.

Enable AI Integrations: Work closely with LLM engineers to design API