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How to become an AI Evals Engineer

Benchmark design, LLM-as-judge, regression suites, and quality metrics. Measure what models actually do before and after every change.

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The AI Evals Engineer roadmap

Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.

Phase 1AI evaluation foundations

Eval-driven development

Shipping AI changes by eyeballing a few examples is how regressions reach users.

Is the change real? Statistics for evals

A few points of movement on a small eval set is often noise, not signal.

Defining success criteria

An eval is only as meaningful as its definition of good.

Phase 2Building eval datasets

Curating a representative eval set

An eval measures only what it contains.

Golden data and label quality

Your eval can never be more correct than its labels.

Preventing train/eval contamination

When eval data leaks into training, a generalization test becomes a memorization test and scores inflate.

Phase 3Grading and metrics

Choosing the right metric

The metric must match the task.

LLM-as-judge

LLM judges scale grading but carry systematic biases - toward length, confidence, position, and their own outputs.

Human evaluation and agreement

Human judgment anchors subjective quality, but only if humans agree.

Phase 4AI evaluation eval infrastructure

Regression suites and CI gates

Known-good behavior breaks silently as prompts and models change.

Reproducible eval harnesses

If the same eval gives different scores each run, no comparison means anything.

Tracing and eval observability

An aggregate score you cannot drill into is a thermometer with no diagnosis.

Phase 5Production and advanced evals

Isolating and diagnosing failures

A wrong answer rarely tells you which component broke.

Red-teaming and safety evals

Passing every functional eval says nothing about behavior under attack.

Online eval and A/B testing

Offline evals measure a proxy; real users measure the outcome.

Reading for this path

The primary sources behind the topics above, all free to read.

Coming from another job?

Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.

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Regulatory compliance, model cards, risk frameworks, and audit trails. Keep AI systems accountable and inside the rules.

AI Infrastructure Engineer

Data pipelines, infrastructure, deployment, observability, and cost control. Keep data, infra, and models reliable and cost-aware.

AI Integration Engineer

APIs, enterprise systems, legacy migration, and AI-feature rollout. Wire AI capabilities into the software businesses already run.

AI Platform Engineer

Internal SDKs, model gateways, routing, and guardrail infrastructure. Build the platform every team ships AI on.

AI Policy Analyst

Regulation, standards, risk, and societal impact. Translate AI policy into what teams must actually build.

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The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.

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