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 toput financial well-being on autopilot to help solve this problem.About the RoleWe’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 OwnAs 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 DoArchitect 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 APIs and data pipelines supporting RAG, embeddings, and model-inference use cases.Design Resilient Data Infrastructure: Implement streaming and async systems (Kafka, Pulsar, or similar) to handle high-volume event traffic.Drive Engineering Quality: Establish patterns for clean code, contracts, testing, and documentation. Lead architecture and code reviews across pods.Mentor and Coach: Elevate senior engineers through structured mentorship, design walkthroughs, and technical guidance.Champion Evolutionary Architecture: Build for change — advocate for modular, observable, and testable systems that can evolve with business needs.Improve Platform Resilience: Implement retry, backoff, rate-limiting, and circuit-breaker patterns to ensure uptime and reliability at scale.Collaborate Cross-Functionally: Work with AI, data, DevOps, and product teams to define shared contracts, SLAs, and infrastructure standards.What We’re Looking ForRequired QualificationsExperience: 8+ years of experience in backend or platform engineering, including 2+ years in high-scale B2C or distributed systems environments.Distributed Systems Mastery: Deep understanding of scalability, consistency, concurrency control, and fault tolerance.Low-Latency Systems Expertise: Proven track record designing systems with strict SLA and sub-second response times.Microservices Architecture: Strong experience building, deploying, and maintaining service-oriented architectures with APIs, event streams, and async messaging.Vector DBs & Embeddings: Hands-on experience with Weaviate, Pinecone, Qdrant, FAISS, or similar; strong grasp of RAG patterns and semantic retrieval.Programming Proficiency: Expertise in Go, Rust, Java, or Python, and familiarity with modern frameworks (gRPC, GraphQL, REST).Data Layer Knowledge: Solid understanding of SQL/NoSQL databases (PostgreSQL, Cassandra, DynamoDB) and caching systems (Redis, Memcached).Resilience & Observability: Experience designing with telemetry, distributed tracing, chaos testing, and monitoring (Prometheus, OpenT