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 depth in ROS2 nodes, RTK GNSS and IMU fusion, CAN/ISOBUS implement control, and crop-versus-weed perception models trained on dusty, variable-light field imagery.
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 sensor fusion drift under canopy, names ISOBUS message layers, and discusses model retraining across crop stages and soil conditions.
02
Evaluation factor
Work that shipped
30% weight
Ask which machines reached real fields: acres covered, rows weeded or fruit picked per hour, uptime during harvest window, and units handed to growers.
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
Cites a deployed platform with season-long field hours, throughput figures, and the design changes forced by actual grower use.
03
Evaluation factor
Diagnosis under uncertainty
20% weight
Test how they debugged failures with no bench: mud-clogged sensors, GPS multipath near tree lines, teleop dropouts, or emergency stop trips mid-row.
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 field failure using rosbag replay and log evidence, isolating root cause rather than swapping parts hopefully.
04
Evaluation factor
Working across the org
15% weight
Check collaboration with agronomists, farm operators and mechanical teams, plus handling of ISO 18497 safety compliance and seasonal deadlines that cannot slip.
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
Describes translating agronomist requirements into specs, training operators, and negotiating scope against a planting or harvest calendar.
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