interface.ai is the industry's-leading specialized AI provider for banks and credit unions, serving over 100 financial institutions. The company's integrated AI platform offers a unified banking experience through voice, chat, and employee-assisting solutions, enhanced by cutting-edge proprietary Generative AI.
Our mission is clear: to transform the banking experience so every consumer enjoys hyper-personalized, secure, and seamless interactions, while improving operational efficiencies and driving revenue growth.
interface.ai offers pre-trained, domain-specific AI solutions that are easy to integrate, scale, and manage, both in-branch and online. Combining this with deep industry expertise,interface.ai is the AI solution for banks and credit unions that want to deliver exceptional experiences and stay at the forefront of AI innovation.
About the Role
Were hiring a Staff Engineer for Nexus , a foundational team building the intelligent backend systems that will enable a new generation of productivity tools and decision-making copilots within financial institutions.
This role is at the intersection of agentic AI, data systems, and human-in-the-loop interfaces . Youll lead engineering efforts to build modular, composable platforms that unify real-time data access, operational intelligence, and natural language interactionabstracting away complexity and enabling institutional teams to operate at unprecedented scale and speed.
Youll work closely with platform teams, infrastructure, product, and AI researchers to ensure these systems are secure, high-performance, extensible , and aligned with the needs of both end-users and developers.
What Youll Do
- Architect and build core backend services that power intelligent AI assistants and operational workflows for enterprise environments
- Design APIs and real-time data integration frameworks to support decision-making and task automation within secure systems
- Lead initiatives around search, routing, orchestration, and domain-specific reasoning enabling fast, accurate, and contextual agent behavior
- Collaborate with infrastructure and AI teams to connect ML capabilities into repeatable, resilient system patterns
- Work on secure-by-default abstractions for identity, access, auditability, and error handling across distributed services
- Enable human-in-the-loop workflows by designing feedback mechanisms, confidence scoring, and intelligent fallback layers
- Drive adoption of scalable engineering practices across services: testing, observability, release safety, and performance tuning
- Act as a technical lead and mentor across engineering, contributing to cross-functional architectural decision-making
What You Bring
Required
- 8+ years of backend or platform engineering experience, with a deep understanding of distributed systems and API-driven architectures
- Experience building real-time data systems, enterprise workflow platforms, or backend infrastructure for intelligent interfaces
- Strong systems design skillsable to make architectural tradeoffs for scale, reliability, extensibility, and velocity
- Proficiency in building large, modular systems in languages such as Node.js, Go, Python , or similar
- Deep familiarity with identity systems, secure access control, and integration with external systems and APIs
- Track record of working across teams and domains to bring technically complex features to life
Preferred
- Experience in enterprise SaaS, compliance-heavy environments, or regulated data systems
- Exposure to AI infrastructure, LLM orchestration, or intelligent assistants
- Understanding of structured knowledge systems, document stores, or semantic search
- Prior work building user-facing automation or internal tools that scaled across departments or clients
Why This Role Matters
- Youll help define the next generation of intelligent tools and assistants used daily by financial institutions and their employees
- Your systems will act as the intelligence and execution backbone behind trusted decision-making workflows and agentic operations
- Youll operate at the convergence of data, systems, and AI designing infrastructure that empowers users, not just software
Compensation
The base salary range for this role will be between $210,000 and $240,000. Final compensation will be determined by a variety of factors, including, but not limited to your location, skills, experience, education, and/or professional certifications. During the phone screening, your recruiter will provide the location-specific salary range for this role. Regardless of your personal situation or where you are in the world, Interface.ai offers comprehensive and great benefits programs to meet your needs:
- Health: medical, dental, and vision insurance and wellbeing resources and programs
- Time away: Public holidays and discretionary PTO package for flexible days off with manager approval
- Financial: 401K, ESPP, Basic life and AD&D insurance, long-term and short-term disability
- Family: parental leave, maternity support, fertility services
- Development: Access to internal professional development resources.
- Additional: 24/7 access to professional counselors, voluntary insurance coverage, exclusive perks and discount
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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How many years of experience with backend or platform engineering experience, with a deep understanding of distributed systems and API-driven architectures *
How many years of experience building large, modular systems in languages such as Node.js, Go, Python, or similar *
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