Evaluate Autonomous Vehicle Safety Specialist candidates across 4 weighted areas: technical depth, work that shipped, diagnosis under uncertainty, and working across the org. Technical depth leads at 35%, so check command of ISO 26262 ASIL decomposition, ISO 21448 SOTIF, HARA and FMEDA methods, plus ODD definition and fault tree analysis for perception. Use the rubric to compare role-specific evidence consistently.
For technical depth, look for evidence the candidate explains ASIL allocation and SOTIF triggering conditions with worked examples, distinguishing systematic faults from performance limitations in perception. For work that shipped, look for evidence the candidate names specific safety cases or safety concepts signed off before public road or shuttle deployment, including scope, ODD limits and residual risk.
Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical depth
35% weight
Check command of ISO 26262 ASIL decomposition, ISO 21448 SOTIF, HARA and FMEDA methods, plus ODD definition and fault tree analysis for perception and planning stacks.
Evidence to listen for
Explains the physics or mechanism behind their work, not just the tooling
Names the standards, tolerances, and constraints they designed against
Can defend a design decision under follow-up questions
Distinguishes what they personally engineered from what the team delivered
Five-point scoring guide
1
Poor
Cannot explain the fundamentals of their own stated specialism.
2
Needs Improvement
Knows the vocabulary but not the underlying mechanism; struggles under follow-ups.
3
Satisfactory
Solid working knowledge for the role; depth thins out on edge cases.
4
Very Good
Strong command of the domain; explains trade-offs and defends decisions well.
5
Excellent
Explains ASIL allocation and SOTIF triggering conditions with worked examples, distinguishing systematic faults from performance limitations in perception.
02
Evaluation factor
Work that shipped
30% weight
Probe safety cases, hazard logs and release gates they authored: GSN argument structures, minimal risk condition designs, disengagement reporting, or safety driver operating procedures actually deployed on fleets.
Evidence to listen for
Names specific programmes, parts, or systems that reached production or field use
States their own scope inside the project
Can give measured outcomes: yield, cycle time, cost, failure rate
Explains what went wrong and what they changed
Five-point scoring guide
1
Poor
No delivered work; experience is coursework, lab-only, or purely observational.
2
Needs Improvement
Contributed to projects but cannot say what shipped or what their part was.
3
Satisfactory
Has delivered real work; outcomes described without numbers.
4
Very Good
Names shipped work and their scope, with some measured results.
5
Excellent
Names specific safety cases or safety concepts signed off before public road or shuttle deployment, including scope, ODD limits and residual risk.
03
Evaluation factor
Diagnosis under uncertainty
20% weight
Test how they investigate ambiguous field events: near-miss triage from drive logs, simulation replay, root cause on an unexpected brake or misclassification, with no reproducible trigger.
Evidence to listen for
Describes a real failure they chased to root cause
Shows a method: isolate variables, reproduce, measure, eliminate
Distinguishes correlation from cause
Says what they ruled out and why, not only what the answer turned out to be
Five-point scoring guide
1
Poor
No diagnostic method; guesses or escalates immediately.
2
Needs Improvement
Trial and error with no structure; cannot explain how they narrowed the cause.
3
Satisfactory
Reasonable method on familiar problems; less structured on novel ones.
4
Very Good
Clear systematic approach with a real root-cause story.
5
Excellent
Walks through a real incident from log evidence to hazard reclassification and validation change, being candid about what stayed unresolved.
04
Evaluation factor
Working across the org
15% weight
Assess how they push safety requirements into perception, planning and validation teams, plus dealings with regulators, TUV assessors, insurers or internal safety review boards.
Evidence to listen for
Explains technical constraints to non-technical stakeholders without condescension
Has negotiated scope, cost, or timeline with manufacturing, product, or suppliers
Documents decisions so others can act on them
Takes review feedback without defensiveness
Five-point scoring guide
1
Poor
Cannot communicate outside their specialism; dismissive of other functions.
2
Needs Improvement
Communication gaps cause rework; avoids stakeholder contact.
3
Satisfactory
Works adequately with other teams; documentation is thin.
4
Very Good
Communicates clearly across functions; reliable collaborator.
5
Excellent
Cites cases where they blocked or reshaped a release, and shows how engineers accepted the requirement rather than routing around it.
Evidence-led prompts
Interview questions for a Autonomous Vehicle Safety Specialist
Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.
01
Can you detail projects where you implemented safety measures in autonomous systems?
02
Describe a challenging vehicle safety issue and how you resolved it.
03
Can you discuss safety audits you have conducted and their outcomes?
04
What is your approach to risk assessment and mitigation in these systems?
05
How do you ensure compliance with the relevant safety standards?
