Staff Software Engineer - AI Platform

POSITION SUMMARY This role is responsible for defining and building the shared AI platform that MeridianLink’s product and engineering teams use to deliver AI-powered features consistently and at scale. You will work across product domains to identify common requirements, design platform services that abstract the hard parts of AI integration, and establish the patterns and standards teams follow when building on top of AI. The right person for this role thinks in platforms, not features, and has strong opinions about what it means to make AI capabilities reliable, observable, and safe in a regulated industry. KEY COMPETENCIES Staff engineers at MeridianLink 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 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 - Reasons fluently across the fundamentals of distributed systems: how systems handle load, how they store large datasets, and how they stay correct under failure - Treats every architecture decision as a trade-off between speed, cost, and correctness, and makes that trade-off explicit when guiding teams; sizes systems with back-of-the-envelope estimation and decides microservices versus monolith on evidence rather than default 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 PLATFORM DESIGN & ARCHITECTURE - Designs shared AI platform services — including model integration layers, prompt management, retrieval-augmented generation infrastructure, embedding pipelines, and vector store management — that product teams can build on without re-solving the same problems - Defines the reference architecture for how AI capabilities are consumed across products, including interface contracts, versioning, and deprecation strategies - Evaluates and selects AI infrastructure components (cloud-managed model services, orchestration frameworks, vector databases) based on reliability, cost, latency, and operational complexity at scale AI RELIABILITY, SAFETY & OBSERVABILITY - Designs evaluation and monitoring pipelines that give teams visibility into AI output quality, latency, cost, and degradation over time - Establishes guardrail patterns and content safety standards appropriate for a regulated financial services environment - Defines the platform’s approach to compliance-relevant concerns: data residency, PII handling in AI pipelines, audit logging for AI-driven decisions PLATFORM ADOPTION & DEVELOPER EXPERIENCE - Works with product engineering teams to understand how they want to consume AI capabilities, then translates that into platform APIs and abstractions that are ergonomic and consistent - Builds the documentation, reference implementations, and onboarding paths that make the platform easy to adopt without hand-holding - Identifies where product teams are solving similar AI problems in isolation and consolidates that work into shared platform capabilities EXPECTED DUTIES AI PLATFORM DISCOVERY & DESIGN - Partner with product engineering teams and architects to catalog current and near-term AI use cases, ide…