Evaluate Vertical Farming Automation 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 check depth in PLC and motion control for grow environments: Siemens TIA Portal or Allen-Bradley logic, VFD-driven gantries and conveyors, fertigation dosing loops. Use the rubric to compare role-specific evidence consistently.
For technical depth, look for evidence the candidate names specific controllers, servo and sensor hardware, and explains tuning of dosing or climate loops with setpoints, tolerances and washdown constraints. For work that shipped, look for evidence the candidate cites commissioned lines with tray-per-hour rates, energy or labour reductions, and honest detail on what was retrofitted after first harvest cycles.
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 depth in PLC and motion control for grow environments: Siemens TIA Portal or Allen-Bradley logic, VFD-driven gantries and conveyors, fertigation dosing loops for EC and pH, IP65 washdown design.
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
Names specific controllers, servo and sensor hardware, and explains tuning of dosing or climate loops with setpoints, tolerances and washdown constraints.
02
Evaluation factor
Work that shipped
30% weight
Probe systems they built and ran in production: tray handling lines, automated seeders or transplanters, SCADA dashboards in Ignition or Priva, plus throughput, kWh per kg and yield per square metre gains.
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 commissioned lines with tray-per-hour rates, energy or labour reductions, and honest detail on what was retrofitted after first harvest cycles.
03
Evaluation factor
Diagnosis under uncertainty
20% weight
Test how they chase intermittent faults in a live grow room: root cause on humidity swings, clogged emitters, vision-based germination misreads, or drives tripping mid-cycle without losing a crop.
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 crop-threatening fault, the data trail (trends, alarm logs, sensor cross-checks) and the containment step taken before the fix.
04
Evaluation factor
Working across the org
15% weight
Assess how they work with growers, food safety and maintenance staff: translating agronomy recipes into control sequences, honouring GMP and hygiene rules, training operators on HMI screens.
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 recipe changes negotiated with head growers, SOPs and HMI documentation written, and technicians who could then handle faults unaided.
Evidence-led prompts
Interview questions for a Vertical Farming Automation Engineer
Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.
01
Can you describe a project where you implemented automation in a growing facility?
02
What experience do you have developing automation systems for controlled growing?
03
How would you handle automation for a multi-tier growing system?
04
Have you worked with horticultural lighting or climate control systems?
05
Do you have experience designing and installing sensor networks?
