AI Mutation Testing Efficiency Optimizer

Ravio Tech

Remote · $80k – $100k / year · Full-time · Posted 4 months ago

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About the role

Mutation testing is the gold standard for measuring test suite quality: introduce small synthetic bugs (mutations) into the source code and measure what fraction the test suite catches. A test suite that catches 85% of mutations has demonstrated real defect detection capability in a way that code coverage cannot. The problem with mutation testing is computational cost: a codebase with 10,000 mutations requires running the test suite 10,000 times, which is prohibitively expensive without optimization. We build test intelligence tooling for 85 engineering teams. Mutation testing with intelligent optimization is the high-value test quality measurement capability we're building. The research literature has established several optimization strategies — equivalent mutation filtering, test prioritization to kill mutations faster, and selective mutation targeting high-risk code regions — but translating these into a production system that delivers meaningful coverage improvement without 10x compute costs is the engineering challenge. You'll build the mutation testing optimization layer: ML-based equivalent mutation detection that skips mutations no test could ever kill, test-mutation relevance scoring that orders test execution to kill mutations with fewer test runs, and code risk scoring that concentrates mutation analysis on the areas where test coverage most matters.

Fully remote.

Responsibilities

Build ML-based equivalent mutation detection identifying mutations that are semantically undetectable by any test
Implement test-mutation relevance scoring ordering test execution to minimize test runs per killed mutation
Develop code risk scoring concentrating mutation budget on high-change-frequency and fault-prone code regions
Create mutation testing execution scheduler managing parallelism and compute cost against quality targets
Build mutation score trend tracking monitoring whether engineering teams are improving test suite quality
Optimize mutation testing latency to under 2 hours for codebases generating 50,000+ mutations

Requirements

3+ years in test engineering, CI infrastructure, or developer tooling with mutation testing or test optimization experience
Strong Python and Docker skills with test execution infrastructure development
Understanding of mutation testing theory, equivalence detection, and test prioritization research
Experience building ML classification systems for code quality prediction
Ability to design optimization systems that make expensive operations tractable without sacrificing validity