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 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 For
Required Qualifications
- Experience: 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, OpenTelemetry).
- Engineering Quality Mindset: Passion for clean code, automated testing, CI/CD, and maintainability.
- Bar-Raising Leadership: Experience mentoring teams, enforcing code quality standards, and elevating design practices.
Preferred Qualifications
- Experience building or scaling real-time personalization or recommendation systems.
- Prior exposure to LLM serving, RAG pipelines, and LLMOps frameworks.
- Familiarity with Kafka, Flink, or Beam for data streaming.
- Contributions to open-source projects in distributed systems or AI tooling.
- Deep understanding of cloud-native architectures (Kubernetes, Istio, Terraform).
What Makes This Role Special
- You’ll define and scale the core technical foundation for AI systems serving millions of users.
- You’ll collaborate with world-class engineers across AI, platform, and product to deliver real-time, intelligent experiences.
- You’ll raise the engineering bar — shaping how code is written, reviewed, and deployed across teams.
- You’ll lead by example: mentoring senior engineers while remaining hands-on in architecture, design, and implementation.
- You’ll be part of an organization where AI-first thinking, evolutionary architecture, and engineering craftsmanship are core values.
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At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible.