Interview scorecard template

Self-Driving Car Engineer interview scorecard

Evaluate Self-Driving Car Engineer candidates across 4 weighted areas: technical depth, work that shipped, diagnosis under uncertainty, and working across the org. Technical depth leads at 35%, so probe the stack layer they own: perception, prediction, planning, or control, and the theory and sensor physics under it. Use the rubric to compare role-specific evidence consistently.

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engineering applied scienceautomotiveautonomous vehiclesperceptionrobotics
TL;DR
For technical depth, look for evidence the candidate deep command of their stack layer and the sensor physics feeding it, including where the models break. For work that shipped, look for evidence the candidate names real driving programmes their code ran in, with intervention or safety metrics they owned. 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

Probe the stack layer they own: perception, prediction, planning, or control, and the theory and sensor physics under it.

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

Deep command of their stack layer and the sensor physics feeding it, including where the models break.

02
Evaluation factor

Work that shipped

30% weight

Look for miles driven with their code in the loop, on public roads or a real test programme, not only in simulation.

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 real driving programmes their code ran in, with intervention or safety metrics they owned.

03
Evaluation factor

Diagnosis under uncertainty

20% weight

Test how they investigate a disengagement when logs, sensors, and the planner all offer a plausible story.

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

Investigates disengagements from logs methodically, separating perception, prediction, and planning causes.

04
Evaluation factor

Working across the org

15% weight

Check how they work with safety drivers, validation, and regulators when a fix has road-safety consequences.

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

Works closely with safety and validation, and documents changes to a standard a regulator could audit.

Evidence-led prompts

Interview questions for a Self-Driving Car Engineer

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Which sensor technologies have you worked with directly on a vehicle: lidar, radar, cameras, IMU, wheel odometry? Tell me about one of their failure modes you had to design around.

  2. 02

    How have you dealt with the challenges of sensor fusion in your past projects, especially when two sensors disagreed?

  3. 03

    What role does SLAM or localisation play in your work, and how accurate did your pose estimate need to be?

  4. 04

    Walk me through your approach to path planning and trajectory generation on a system you actually worked on.

  5. 05

    What do you keep in mind for the computational efficiency of your algorithms, given the compute budget on the vehicle?

See the complete Self-Driving Car Engineer question set
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