AI Technical Debt Quantification Developer

Inovex Intelligence

Hybrid · $130k – $160k / year · Full-time · Posted 4 months ago

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About the role

Engineering teams know they have technical debt. They rarely know how much, where it's concentrated, what it's costing them in velocity, or how to prioritize paying it down against new feature work. Technical debt that can be quantified in terms of maintenance cost, bug rate correlation, and velocity drag becomes a resource allocation argument. Technical debt that remains qualitative stays on the backlog indefinitely. We build developer intelligence tooling used by 80,000 engineers daily. Technical debt quantification is the capability we're building to sit alongside our existing code review and search features: analyzing codebases to identify debt patterns (complex methods, high coupling, inadequate test coverage, outdated dependencies) and expressing their cost in developer-hours and engineering velocity terms that engineering managers can use in planning conversations. You'll design and build the debt quantification system: static analysis integration that identifies structural debt markers, LLM-based code complexity assessment that catches semantic debt that rules don't catch, and the cost estimation models that translate debt signals into projected maintenance burden. The role is hybrid — 2 days per week in our SF office.

Responsibilities

Design technical debt detection combining static analysis rules and LLM-based code complexity assessment
Build debt pattern classification covering coupling, cohesion, complexity, test coverage, and dependency age
Implement maintenance cost estimation projecting developer-hours from detected debt patterns
Develop velocity drag correlation connecting debt concentration to slower development in affected areas
Create debt prioritization ranking by estimated ROI on remediation versus maintenance cost savings
Build debt trend tracking monitoring whether engineering teams are accumulating or reducing debt over time

Requirements

6+ years in software engineering with developer tooling, static analysis, or engineering productivity experience
Deep understanding of technical debt patterns and their operational consequences
Strong Python skills with code analysis and LLM integration experience
Experience building tools that help engineering teams make prioritization decisions
Ability to design quantification systems that earn trust through empirical validation