Job Opportunities API

Position Summary This role is the technical lead for MeridianLink’s customer-facing AI product engineering. The Staff Software Engineer - AI Products sits on our AI Products team and owns the architecture and delivery of intelligent features going directly into the hands of credit union clients. This is the first generation of AI-native products at MeridianLink, and this engineer sets the technical bar for how those products are built: from feature architecture and LLM integration patterns to evaluation quality and production reliability. They partner closely with the AI Platform team to leverage foundational infrastructure and with Product Management to translate business intent into well-designed AI features. Key Competencies What it means to be a Staff Engineer at MeridianLink Staff engineers operate across multiple teams or an entire product line. They set technical direction, make architecture and technology decisions that others build against, and raise the engineering floor across the teams they touch. Staff engineers at MeridianLink are active, daily users of AI-assisted development tools -- and go further by building the workflows, tooling, and patterns that make those tools more effective for the teams around them. Technical Leadership & Architecture - Makes critical architecture and design decisions that span multiple teams or an entire product area - Evaluates technology choices with a clear view of trade-offs at scale, not just for the immediate problem - Drives technical standards and patterns that other engineers can follow without being supervised - Identifies systemic problems before they become incidents Cross-Team Execution - Provides day-to-day technical direction for one or more scrum teams without holding a management title - Steps into ambiguous, high-stakes technical problems across teams and drives them to resolution -- without being asked - Holds a high bar in code and design review across team boundaries AI Feature Architecture & Quality - Designs customer-facing AI features with reliability, correctness, and user trust as primary constraints - Defines evaluation and testing standards for LLM-integrated systems, including prompt regression testing, output quality metrics, and human evaluation criteria - Architects AI features to degrade gracefully when model outputs are low-confidence or unexpected, maintaining a reliable user experience in production - Balances AI capability decisions against compliance constraints relevant to regulated financial services AI Product Engineering - Applies deep practical knowledge of LLM application patterns: prompt engineering, context management, RAG pipelines, agentic workflows, and provider integration - Makes informed decisions about AI capability design: when to use retrieval vs. fine-tuning, when to call the model vs. use deterministic logic, and how to structure multi-step AI workflows - Works fluently across the full stack of AI product delivery -- from backend LLM integration to the frontend surfaces users see - Interfaces with the AI Platform team to consume shared infrastructure and feeds real-world product requirements back into platform prioritization Product Partnership & Stakeholder Influence - Partners with Product Management to translate business requirements and user needs into concrete AI feature designs, contributing technical feasibility while incorporating market and customer context - Communicates architectural tradeoffs and product constraints clearly to non-technical stakeholders, including product leadership - Produces RFCs and ADRs that capture durable decisions for AI features and serve as shared reference for future product work - Shapes the roadmap of AI feature investment by surfacing technical risk, capacity constraints, and platform dependencies early Expected Duties AI Feature Architecture & Technical Direction - Own the reference architecture for customer-facing AI features, i