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

We are building auniversal 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 proprietaryBankGPT platform, this assistant is positioned todisplace age-old legacy systems within financial institutions andown the end-to-end CX stack,unlocking a $200B opportunity and potentiallyreplacing 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 Role

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

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

The ideal candidate is asystems thinker who thrives on solving large-scale engineering challenges in distributed, event-driven environments — someone who obsesses overperformance, 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 thetechnical backbone for the Core Platform team — defining architecture, mentoring teams, and ensuring engineering excellence across all systems.
You’ll focus on:

Designing and scalinglow-latency, fault-tolerant distributed systems serving real-time workloads.
Architectingmicroservices and event-driven systems that are secure, composable, and resilient under scale.
IntegratingVector Databases and Embedding Stores to support intelligent retrieval, RAG (Retrieval-Augmented Generation), and adaptive AI experiences.
Partnering with AI and Product teams to embedLLMs and inference services into the Core Platform, ensuring performance and observability.
Definingtechnical standards, best practices, and evolutionary architecture patterns across teams.
Driving continuous improvement incode quality, observability, and deployment reliability.
Acting as atechnical 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 ofscalability, 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 withWeaviate, Pinecone, Qdrant, FAISS, or similar; strong grasp ofRAG patterns andsemantic retrieval.
Programming Proficiency: Expertise inGo, 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 scalingreal-time personalization or recommendation systems.
Prior exposure toLLM serving, RAG pipelines, and LLMOps frameworks.
Familiarity withKafka,Flink, orBeam for data streaming.
Contributions to open-source projects in distributed systems or AI tooling.
Deep understanding ofcloud-native architectures (Kubernetes, Istio, Terraform).

What Makes This Role Special

You’ll define and scale thecore technical foundation for AI systems serving millions of users.
You’ll collaborate with world-class engineers across AI, platform, and product to deliverreal-time, intelligent experiences.
You’llraise the engineering bar — shaping how code is written, reviewed, and deployed across teams.
You’ll lead by example: mentoring senior engineers while remaininghands-on in architecture, design, and implementation.
You’ll be part of an organization whereAI-first thinking,evolutionary architecture, andengineering 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.

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