Hiring nowAI robustness, safety engineering and incidents

AI Robustness Engineer

Also called Robust ML Engineer

An AI Robustness Engineer tests how a model behaves when inputs change or the world shifts, then builds and checks fixes. It matters because customers meet the edge cases long before your benchmarks do.

What they do

They design stress tests that push a model with noisy, adversarial, out-of-range and shifted inputs. They run those tests, compare behaviour against a baseline and work out which failures matter most. They then try mitigations such as data changes, input checks, fallback logic and abstention thresholds, and test whether each one holds. They check that confidence scores mean something under shift. They log failures in the incident system, link them to causes and write up findings for engineers and product owners in plain language.

What a strong hire delivers

In the first months, a strong hire gives you a stress-test suite that runs against every release candidate. They produce a robustness report that ranks failure modes by consequence, not by how interesting they are. They deliver mitigations that have been tested, with evidence of what they fix and what they cost. They also leave behind agreed abstention behaviour and a clear route from a failure to a logged incident.

How you can tell they are good

Ask them to walk through a robustness failure they found and what they did about it. Strong people describe the shift, the measured degradation and the trade-offs of the fix. They separate average performance from worst-case behaviour. Ask how they would know a model's confidence can be trusted. Be wary of anyone who only talks about adversarial attacks or only quotes headline accuracy.

Why it is hard to fill

The title is rare and the work sits between disciplines. Most candidates are strong in one part, such as applied ML, reliability or risk, and thin in the others. Many people doing this work carry titles like evaluation engineer or safety engineer. Few have shipped mitigations rather than just reported problems. Expect to assess skills, not job titles, and to be flexible on background.

Where to find them

Look at reliability and safety engineers from regulated or high-consequence industries who have moved into machine learning. Applied ML engineers who built evaluation and monitoring for production models are another source. So are risk and assurance specialists with technical depth. They sit in AI product companies, model providers, safety teams and larger firms that run models in customer-facing systems.

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 Robustness Engineer

  1. Tell me about a time you found a model failing under distribution shift. How did you find it and what changed afterwards?

    A strong answer names the shift, how it was detected, the measured degradation and the fix that shipped. It shows follow-up checks. Worry if the story ends at the diagnosis, or if there is no measurement behind it.

  2. How would you design a stress-test suite for a model that has just gone into a customer-facing product?

    Look for a plan driven by likely real inputs and consequences: noise, formatting changes, out-of-range cases, adversarial inputs. Good answers cover coverage, baselines and release gating. Worry about generic lists of attacks with no link to the product.

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

Job description you can post

We are hiring an AI Robustness Engineer to test how our models behave when inputs change and conditions shift, and to build mitigations that hold up. You will work with engineering, product and safety to reduce consequential failures before customers meet them.

What you will do

  • Design and run stress tests for model behaviour under input variation and distribution shift
  • Measure performance degradation and report it against agreed baselines
  • Develop and test mitigations, including data changes, input checks and fallback logic
  • Set and validate calibrated abstention behaviour
  • Log failures in our incident system and trace them to causes
  • Write robustness reports that engineers and product owners can act on

What you will bring

  • Demonstrated work testing ML models beyond standard held-out evaluation
  • Experience building or running evaluation tooling
  • Evidence of mitigations you tested and shipped
  • Working knowledge of uncertainty quantification and calibration
  • Experience with incident response or reliability practice
  • Clear written communication of technical risk

Nice to have

  • Background in safety engineering or risk management in a high-consequence field
  • Experience with agent frameworks or multi-step model systems
  • Experience setting release gates based on test results

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

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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.

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