A ready-to-use scorecard for engineering interviews: weighted criteria, a clear rating scale, sample questions, and how to turn it into a live, scored rubric.
A hiring scorecard turns a vague impression of a candidate into a defensible decision. For software engineering roles, that matters more than almost anywhere else: technical depth, problem solving, and collaboration are easy to feel but hard to compare across five interviewers who each saw a different slice of the candidate. A shared scorecard fixes the criteria before anyone meets the candidate, so every interviewer rates the same things on the same scale.
This template gives you the full structure: the criteria worth measuring for an engineer, a four-point rating scale with anchored definitions, sample questions mapped to each criterion, and a simple way to combine the scores into a recommendation. Copy it into a doc and use it as is, or read on to see how Lehire turns the same structure into a live rubric that produces an evidence-based 0-100 fit score per candidate.
An engineering hiring scorecard is a structured evaluation form that defines the specific competencies a software engineer is assessed on, such as coding ability, system design, problem solving, and collaboration, each with a fixed rating scale. Every interviewer scores the same criteria, which makes candidate comparisons consistent and decisions evidence-based rather than driven by gut feel.
A good engineering scorecard measures a small set of high-signal competencies rather than a long checklist. We recommend six. Coding and implementation: writes correct, readable code, chooses sensible data structures, handles edge cases, and tests their own work. Problem solving: breaks an ambiguous problem into parts, reasons about tradeoffs out loud, and adapts when a first approach stalls. System design and architecture: scopes requirements, reasons about scale, data flow, and failure modes, and justifies design choices (weight this higher for senior roles).
Technical depth in the stack: demonstrates real, hands-on understanding of the languages, frameworks, and tools the role requires, not just surface familiarity. Collaboration and communication: explains technical decisions clearly, gives and takes feedback well, and works with non-engineers. Ownership and craft: cares about quality, debugging discipline, code review, and follows through on commitments.
Weight the criteria to the role. For a junior engineer, weight coding and problem solving most heavily and treat system design as a bonus. For a senior or staff engineer, raise system design, ownership, and communication, and treat raw coding as table stakes. A simple weighting scheme: assign each criterion a percentage that sums to 100, for example coding 25, problem solving 20, system design 20, technical depth 15, collaboration 10, ownership 10.
Use a four-point scale with anchored definitions so a "3" means the same thing to every interviewer. Avoid five-point scales: the middle option becomes a place to hide indecision. Force a lean either above or below the bar.
1, Strong no: clear evidence the candidate is below the bar on this criterion. Concrete gaps, not just nerves. 2, Lean no: some relevant ability but notable weaknesses; would need significant ramp or support. 3, Lean yes: meets the bar with solid, demonstrated evidence; would perform the role competently. 4, Strong yes: clearly exceeds the bar; you would actively fight to hire them on this dimension.
Pair every rating with required evidence. The rule is simple: no score without a specific observation. "Gave a 4 on system design because they unprompted identified the read-heavy access pattern and proposed a cache with a clear invalidation strategy" is usable. "Seemed strong, good vibes" is not.
Coding and implementation: "Implement a function that merges overlapping intervals; walk me through your approach before you code." Watch for clarifying questions, edge case handling (empty input, touching intervals), and whether they test the result. Problem solving: "You have a service whose p99 latency suddenly tripled overnight. How do you investigate?" You are scoring the structure of the investigation, not a memorized answer.
System design: "Design a URL shortener that handles 10,000 writes and 1M reads per second. Talk me through storage, key generation, and how you would scale reads." Technical depth: ask a pointed question inside their claimed stack, for example "How does the event loop decide what runs next, and what happens when you block it?" Collaboration: "Tell me about a time you disagreed with a teammate on a technical decision. What did you do?"
Ownership and craft: "Describe a production incident you were responsible for. What broke, how did you find it, and what did you change so it would not happen again?" Listen for accountability, a real debugging method, and a durable fix rather than a one-off patch.
Each interviewer fills the scorecard independently and before the debrief, never after hearing others. This is the single most important rule: shared scores written after a group conversation just measure who spoke loudest. Independent scores first, discussion second.
To combine scores, take the weighted average of each interviewer's criterion ratings, then look at the spread across interviewers. A candidate who is a consistent 3 across five interviewers is a safer hire than one who is a 4 from two people and a 1 from three. Large disagreement is a signal to dig in, not to average away.
Hold a structured debrief: go criterion by criterion, surface the evidence behind each score, and let the people who scored at the extremes explain why. End with an explicit recommendation per interviewer (strong hire, hire, no hire, strong no hire) and a single hiring decision owner who makes the call. Record the reasoning, not just the outcome, so the next time a similar candidate appears you can compare.
Your scorecard criteria and weights become a structured rubric every interviewer fills in Lehire, so scores are consistent and comparable by design.
Lehire combines criterion ratings and interview signal into one defensible fit score per candidate, with the evidence attached to every dimension.
The Decision Engine ranks your shortlist against the same rubric, so you compare engineers on the same axes instead of on whoever interviewed last.
The AI Interviewer can run a first technical screen and scorecards capture structured signal, so no observation is lost between rounds.
Every scorecard and decision is retained, so a strong candidate who did not get this role resurfaces for the next one with their evidence intact.
See which criteria actually predict success, where interviewers disagree, and where your engineering bar drifts over time.
A spreadsheet scorecard is a great start. Here is what changes when the same structure runs inside a hiring decision intelligence platform.
Give every interviewer in a phone screen, technical, and onsite the same criteria so signal accumulates instead of resetting each round.
New interviewers learn the bar fast when the rubric is explicit and they can see how their scores compare to calibrated colleagues.
When a hire is questioned, you have the criteria, the scores, and the evidence on record rather than a recollection of how it went.
Aim for four to six high-signal criteria. More than that and interviewers stop reading the definitions and start rating on overall impression, which is exactly what a scorecard is supposed to prevent.
Use the same criteria but different weights. For junior roles, weight coding and problem solving heavily and treat system design as a bonus. For senior and staff roles, raise system design, ownership, and communication.
A five-point scale gives interviewers a neutral middle to hide in. A four-point scale forces a lean above or below the bar, which produces clearer, more comparable signal.
No. Each interviewer should score independently before any discussion. Shared scores written after a group conversation mostly measure social influence, not the candidate.
A take-home measures one slice of ability. A scorecard is the framework that turns every signal, including the take-home, into a consistent, weighted evaluation across the whole loop.
Yes. In Lehire you load these criteria and weights once, and every interviewer scores against them, producing an evidence-based fit score and a ranked shortlist instead of a static file.
Load your engineering criteria once and let Lehire collect consistent, evidence-based scores and rank your shortlist on a single fit score.