Hiring nowAI robustness, safety engineering and incidents

AI Safety Engineer

Also called AI Systems Safety Engineer

An AI Safety Engineer turns the ways an AI system could hurt users or the business into specific tests and controls. That gives the company evidence for release decisions, rather than a gut feeling about whether the system is safe enough.

What they do

They start by mapping how a system can fail and who is affected. Then they turn each hazard into a control and a test that can pass or fail. They build stress tests for shifted inputs, misuse and edge cases, and run them against model, prompt and tool changes. They work with engineers on mitigations, track which controls are covered, and write down the risk that remains. When something goes wrong in production, they help trace the cause and check that the fix holds. They keep hazard records current as the product changes.

What a strong hire delivers

In the first months, a strong hire produces a hazard analysis for the main product that engineers and leadership both understand. They set up a repeatable safety test package that runs before release. They agree a clear view of control coverage and which residual risks are accepted, by whom. They also tighten how incidents are logged, reviewed and fed back into tests.

How you can tell they are good

Ask them to walk through a hazard analysis they wrote and show how one hazard became a test. Good candidates explain what the test cannot catch. They separate severity from likelihood, and they can say when they would block a release. Be wary of anyone who talks only about model benchmarks or only about paperwork.

Why it is hard to fill

The title is new and used loosely. Some companies mean red teaming, others mean compliance, others mean classic safety engineering. The strongest people often sit in other fields, so job titles rarely match. Few have shipped safety evidence for a machine learning system, and those who have are usually settled. Candidates need both engineering skill and the judgement to say no to a launch.

Where to find them

Look in reliability and safety engineering in sectors where failures are costly, such as automotive, aerospace, medical devices and industrial control. Risk and assurance teams, site reliability and incident management, and applied ML teams with evaluation duties also produce good candidates. Frontier labs and model providers have some, as do AI product companies with regulated customers. Ask about outcomes, not job titles.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Safety Engineer

  1. Describe a hazard analysis you wrote. How did you turn the hazards into tests and controls?

    A strong answer names real hazards, shows how each became a testable control, and explains who reviewed it. Worry if they describe only a document, or cannot link any hazard to a test that ran.

  2. How do you decide whether a system is safe enough to release?

    Good answers use severity, likelihood, control coverage and residual risk, and name who accepts the remainder. Worry about vague confidence, a single score, or no willingness to block a release.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring an AI Safety Engineer to make the risks in our AI products visible, testable and managed. You will turn hazards into controls and release evidence, and work closely with engineering, product and leadership so launch decisions rest on facts.

What you will do

  • Analyse our AI systems for hazards and record them clearly
  • Turn hazards into controls and pass or fail tests
  • Build and maintain stress tests for shifted inputs, misuse and edge cases
  • Produce safety evidence packages for releases
  • Track control coverage and unresolved residual risk
  • Support incident response and feed lessons back into tests
  • Advise teams on mitigations and trade-offs

What you will bring

  • Shown work producing hazard analyses or safety cases for complex systems
  • Experience designing validation tests and acting on their results
  • Working knowledge of model evaluation methods
  • Ability to reason about uncertainty and explain it simply
  • Experience with incident handling and root cause analysis
  • Clear written communication with technical and non-technical readers

Nice to have

  • Background in safety-critical or regulated engineering
  • Experience with agent systems or tool-using models
  • Experience running adversarial or red team exercises

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Uncertainty quantificationAdvancedEstimate confidence, calibration and model uncertainty
  • Model evaluationAdvancedMeasure capability and failure modes credibly
  • Reliability and incident responseWorkingDetect failures, restore service and prevent recurrence
  • AI risk managementWorkingIdentify, assess, treat and monitor AI risks

Other roles in aI robustness, safety engineering and incidents

Hiring nowAI Robustness EngineerAn AI Robustness Engineer tests how a model behaves when inputs change or the world shifts, then builds and checks fixes. It matters because…EmergingAI Incident Response LeadAn AI Incident Response Lead coordinates the response when a deployed AI system fails or causes harm. They contain the damage, find the root cause…EmergingAI Reliability Evaluation ScientistAn AI Reliability Evaluation Scientist measures how consistently an AI system works and how likely it is to fail on realistic workloads. It gives…

Related roles in other families

Hiring nowAI Site Reliability EngineerAI observability, reliability and FinOpsAn AI Site Reliability Engineer keeps services that depend on models available and recoverable under real traffic. They matter because customers…EmergingAI Observability EngineerAI observability, reliability and FinOpsAn AI Observability Engineer instruments model and agent workflows so teams can see what is happening in production. It matters because failures…Hiring nowAI FinOps AnalystAI observability, reliability and FinOpsAn AI FinOps Analyst measures what each useful AI task costs and finds ways to bring it down without hurting quality. They give the company a clear…Hiring nowAI Capacity Planning EngineerAI observability, reliability and FinOpsAn AI Capacity Planning Engineer plans how much compute the company needs, when, and at what cost. They keep services reliable at peak load without…

How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

← AI robustness, safety engineering and incidentsAll new job families

Start a search

Hiring an AI Safety Engineer?

Tell us the work. We find the people already doing it, whatever their job title says, and you meet them already interviewed for your role.