SkillHack

SkillHack › Career guides › AI Safety Engineer

How to become an AI Safety Engineer

Risk analysis, red teaming, privacy, policy, evaluation design, and monitoring. Probe, evaluate, and govern AI systems.

Start this path free

The AI Safety 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 safety foundations

AI safety basics

Safety is not a feature you bolt on; it is the concrete set of ways this particular product could cause harm - bad advice, leaked data, abuse - enumerated and prioritized.

Alignment versus safety

Alignment asks whether the model wants what we want; system safety asks whether the deployment stays within acceptable risk even when the model, its tools, or its users misbehave.

Framing and triaging risk

You will always have more risks than time.

Phase 2AI safety guardrails

Input and output safety

Input checks and output checks fail differently: input checks stop prompt-injection and disallowed requests before a call; output checks catch harmful or leaking generations however they arose.

Content moderation

For a small set of clearly-defined prohibited categories, a purpose-built moderation classifier with a human-review queue beats asking the generation model to police itself or matching keywords.

Refusals and over-refusal

Over-refusal is a real failure too: an assistant that will not explain a common drug is broken, even if safely so.

Phase 3AI safety evaluation

Safety evals

A passing safety score only means something if the eval is representative, uncontaminated, and graded against a written policy.

Red-teaming

Red-team time is scarce; aim it where a found flaw matters most - the high-consequence tool paths where the agent can delete or exfiltrate - rather than spreading evenly or re-confirming refusals you…

Jailbreak resistance

Blocking one jailbreak prompt is not resistance.

Phase 4AI safety agent

Excessive agency

Excessive agency is when an agent's permissions exceed its task.

Human in the loop

Gate humans on the actions that are hard to reverse and wide in blast radius - money, deletion, customer email, prod config - and let low-stakes, easily-undone actions run free.

Sandboxing

When an agent runs generated code, contain it by construction: an isolated sandbox with least privilege, no host secrets, allowlisted egress, and resource limits.

Phase 5AI safety governance

Safety policies

An enforceable policy is testable: concrete allowed/disallowed examples and edge-case handling so a grader and the model reach the same verdict on the same input.

Monitoring and incident response

In a live safety incident, contain first: deploy a targeted mitigation or rollback to stop ongoing harm and capture the reproducing input, then assess scope, communicate, and work the durable fix and…

Documentation

The most valuable safety documentation lets a reader judge fitness for a use: intended use and limitations, the risks considered and their mitigations, what was evaluated and the results, and the…

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.

AI Security Engineer

Prompt injection defense, model supply chain, data leakage, and access control. Secure AI systems end to end.

AI Solutions Architect

System design, model selection, build-vs-buy, and scaling and cost. Architect enterprise AI solutions that hold up in production.

AI Strategist

Opportunity sizing, build-vs-buy, and adoption strategy. Decide where AI creates real business value.

AI Systems Engineer

GPUs, distributed training, kernels, and memory and throughput optimization. Make large models train and serve fast.

AI UX Designer

Human-AI interaction patterns, trust, uncertainty, and feedback loops. Design interfaces where people and models work well together.

Analytics Engineer

Data modeling, transformation pipelines, metrics, and data quality. Turn raw data into trusted datasets teams can build on.

All career guides

Start the AI Safety Engineer path for free

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.

Start free

No payment, no credit card, no CV. Sign in with Google.