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AI Evaluation Specialist

Evaluation and engineering, a mid-level role

What does AI Evaluation Specialist do?

Designs and runs the tests that show how an AI system behaves under realistic, difficult and adversarial conditions, and helps teams decide whether results support launch, restriction, remediation or rejection.

What it decides: What a score measures, what it misses, and whether the evidence supports release.

Open now: 6 postings at tracked AI employers match this seat as of September 28, 2026, listed on the jobs page and counted weekly by the Hiring Index.

At physical AI employers: 6 postings match this seat as of September 28, 2026, up 1 since September 21, 2026, counted weekly by the Physical AI Hiring Index.

Where the jobs areInterview questionsCheck my readiness

The competencies employers name

  • AI evaluation and testing designcore, depth expected

    Designs tests for factuality, robustness, fairness, safety and abuse resistance with rubrics, baselines and thresholds, and says what a score misses.

    19 graded topics teach this

  • Model failure modes and bias recognitioncore, depth expected

    Recognizes hallucination, drift, skew, brittleness and biased outcomes, and knows how each one enters a system.

    19 graded topics teach this

  • How models work, at a governance depthcore, depth expected

    Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.

    20 graded topics teach this

  • AI security fundamentalsrequired, working knowledge

    Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.

    22 graded topics teach this

  • Post-deployment monitoring and drift detectionrequired, working knowledge

    Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.

    15 graded topics teach this

  • Evidence collection and audit-ready documentationrequired, working knowledge

    Collects, labels and preserves the evidence that a control operated, a decision was made, and a claim can be defended to an auditor or regulator.

    23 graded topics teach this

  • Explainability, transparency and contestabilitypreferred, working knowledge

    Decides what a person affected by an AI decision must be told, how an output can be explained, and how they can challenge it.

    23 graded topics teach this

  • Agentic AI controls and authorization boundariespreferred, working knowledge

    Governs AI agents that take actions: tool access, least privilege, interruptibility, cascading actions and accountability for what an agent did.

    23 graded topics teach this

Where it is taught

Counted from the graded topics that teach this role's competencies. Your own path is shorter: it skips what you already cover.

Plus 2 topics across 1 other program, which the path includes only when nothing else teaches a gap.

Check your readiness for this role

What you already have: your background and your CV (both optional, both count)
Signed in? Every topic you have passed already counts as proof.

Roles that feed into it

  • QA Engineer
  • Data Scientist
  • Research Analyst
  • Trust and Safety Specialist
  • AI Model Validator
  • Domain expert with analytical skills

Where it leads

Backgrounds that reach it fastest

What postings tend to name

Frameworks: NIST AI RMF MEASURE, NIST AI 600-1.

Credentials often listed: AIGP. GAGE does not issue these and does not prepare for their exams; the record you earn here is your own graded evidence, which stands beside them.

Questions

Why can conventional software testing not cover AI?
Outputs vary by prompt, context, data, user and model version. Evaluation has to describe a probabilistic system with scenarios, rubrics, thresholds and subgroup tests, and say plainly what the number leaves out.
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