The Opportunity
AI code testing is a new and rapidly growing field. Companies need engineers who can verify AI-generated code, build testing frameworks, and ensure quality. This lesson covers how to build a portfolio that showcases your AI code testing expertise and position yourself for AI Code Testing Engineer, AI QA Engineer, SDET for AI, and related roles.
The 6 AI Code Testing Job Roles
| Role | Salary | What You Do | Key Phases | Top Skills |
|---|
| AI Code Testing Engineer | $130k-$190k | Test, verify, validate AI code before production | 0-4, 7 | PBT, Mutation, SAST, CI/CD gates |
| AI QA Engineer | $120k-$290k | Design eval frameworks, build test infrastructure | 0-3, 5, 8-9 | Eval design, PBT, Adversarial, Observability |
| SDET for AI Code | $140k-$220k | Blend test automation with AI code expertise | 1-4, 7-8 | Test automation, Contract testing, Mutation |
| AI Security Testing Engineer | $150k-$230k | Security testing of AI-generated code | 2, 5-6 | Z3, Semgrep/SAST, CWE, Slopsquatting |
| AI Code Quality Architect | $170k-$280k | Design testing strategy for AI coding teams | 0, 2, 7-9 | Verification strategy, Tool selection, Thresholds |
| DevSecOps for AI Code | $150k-$220k | Integrate security gates into AI pipelines | 2, 5-7 | CI/CD security, Supply chain, SBOM |
Why AI code testing is a high-demand field
55.8% of AI-generated code contains vulnerabilities. 22.7% of AI-introduced issues survive 9+ months. Companies are realizing that AI coding tools don't replace testing -- they increase the need for testing. Engineers who can verify AI code are in high demand. The field is new (AI coding tools became mainstream in 2024-2025), so there's less competition than traditional SDET roles. Getting in early positions you as an expert as the field grows.
What are the 7 interview tests for AI code testing roles, and what does each test?
The 7 interview tests are designed to evaluate a candidate's AI code testing expertise across the full spectrum of skills taught in this course. Each test maps to specific phases and lessons: (1) Write a property-based test suite for an AI-generated function: What it tests: PBT knowledge and property derivation skills. Mapping: Phase 3 (Property-Based Testing for AI Code). Task: Given an AI-generated function, write Hypothesis property tests covering: (a) Roundtrip property: f(g(x)) == x (e.g., encode/decode roundtrip). (b) Monotonicity: if x > y then f(x) > f(y) (e.g., sorting is monotonic). (c) Bounded output: f(x) is always within [min, max] (e.g., percentage is 0-100). (d) Edge cases: empty input, None, negative, max value. Run the tests and report any failures. What the interviewer looks for: (a) Can the candidate identify properties (not just examples)? (b) Can the candidate write Hypothesis strategies? (c) Does the candidate understand the difference between PBT and example-based testing? (d) Can the candidate interpret PBT failures? (2) Run mutation testing and achieve >80% mutation score: What it tests: Mutation testing proficiency. Mapping: Phase 4 (Mutation Testing & Behavioral Verification). Task: Given an AI-generated codebase with existing tests: (a) Run mutmut to generate mutants. (b) Identify surviving mutants (tests don't catch them). (c) Write tests to kill surviving mutants. (d) Achieve >80% mutation score. (e) Report the mutation score before and after. What the interviewer looks for: (a) Can the candidate run mutmut and interpret results? (b) Can the candidate identify why mutants survive (weak tests)? (c) Can the candidate write tests that kill specific mutants? (d) Does the candidate understand that coverage != test quality? (3) Find security vulnerabilities in AI-generated code: What it tests: SAST and security analysis skills. Mapping: Phase 2 (Static Analysis) and Phase 5 (Security Testing). Task: Given an AI-generated codebase: (a) Run Semgrep + Bandit (SAST). (b) Manually review for vulnerabilities SAST misses. (c) Identify CWE-pattern vulnerabilities. (d) Write a security report with severity, CWE ID, and fix for each finding. What the interviewer looks for: (a) Can the candidate run SAST tools? (b) Can the candidate identify vulnerabilities beyond SAST (novel patterns)? (c) Does the candidate understand CWE classifications? (d) Can the candidate write actionable security reports? (4) Build a CI/CD quality gate for an AI-generated PR: What it tests: CI/CD and quality gate engineering. Mapping: Phase 7 (CI/CD Pipelines for AI Code). Task: Design a GitHub Actions workflow that runs: (a) Lint (Ruff). (b) SAST (Bandit + Semgrep). (c) Test generation (AI-generated tests). (d) Quality scoring (coverage + mutation). (e) Intent verification. The workflow should block merge if any quality gate fails. What the interviewer looks for: (a) Can the candidate design a CI/CD workflow? (b) Does the candidate understand quality gates? (c) Can the candidate write GitHub Actions YAML? (d) Does the candidate understand the balance between thoroughness and speed? (5) Detect hallucinated packages in an AI-generated codebase: What it tests: Slopsquatting defense skills. Mapping: Phase 6 (Supply Chain Security & Slopsquatting). Task: Given an AI-generated project with dependencies: (a) Identify which packages are hallucinated. (b) Verify each package against PyPI registry. (c) Build a dependency allowlist. (d) Report findings with correct package names. What the interviewer looks for: (a) Does the candidate understand slopsquatting? (b) Can the candidate identify hallucinated packages? (c) Can the candidate verify packages against registries? (d) Can the candidate build an allowlist? (6) Perform intent verification on an AI-generated PR: What it tests: Intent verification methodology. Mapping: Phase 7 (Lesson 4: Intent Verification) and Phase 8 (Lesson 5: Intent Verification prompt). Task: Given a PR description and code diff: (a) Identify intent-mismatch findings (code does something different from PR claims). (b) Identify silent-scope findings (code changes files not mentioned in PR). (c) Identify unbacked-claim findings (PR claims to fix bug but no test). (d) Identify doc-drift findings (code changes without doc updates). (e) Identify missing-impl findings (PR claims feature but implementation incomplete). What the interviewer looks for: (a) Does the candidate understand intent verification? (b) Can the candidate identify the 5 types of intent mismatches? (c) Can the candidate compare PR description with diff? (d) Does the candidate understand why intent verification is AI-specific? (7) Design a testing strategy for a team using AI coding assistants: What it tests: Strategic thinking and framework design. Mapping: Phase 8 (Testing Framework & Continuous Execution) and Phase 9 (Production Monitoring). Task: Given a team scenario (10 engineers, 40 PRs/day from AI agents): (a) Design the verification pyramid (which tests to run, when). (b) Select tools (pytest, Hypothesis, mutmut, Semgrep, Z3). (c) Set quality thresholds (coverage, mutation score, SAST). (d) Define CI/CD gate policy (block vs warn). (e) Define monitoring strategy (error rate, drift, issue survival). What the interviewer looks for: (a) Can the candidate design a comprehensive testing strategy? (b) Does the candidate understand the verification pyramid? (c) Can the candidate select appropriate tools? (d) Can the candidate set reasonable quality thresholds? (e) Does the candidate understand the tradeoff between thoroughness and speed? (f) Can the candidate think at the team/org level, not just individual code? In summary, the 7 interview tests cover the full spectrum of AI code testing: PBT (1), mutation testing (2), security (3), CI/CD (4), supply chain (5), intent verification (6), and strategy (7). Each test maps to specific phases of the course, ensuring that course graduates are prepared for all 7 tests."