Improve quality upstream. Turn every requisition into a scorecard of the few skills that matter, have people who do the work assess against it, set a pass mark before you see candidates, ask for written evidence rather than impressions, and track what happens after the hire so the scorecard improves.
Most efforts to improve technical hiring start at the interview: a harder coding test, an extra round, a new panel. These help less than expected, because the quality problem usually starts earlier, with a vague requisition, and continues later, because nobody checks whether the people hired turned out as expected.
The fixes below are mostly about structure and evidence. None of them needs a new tool.
What does “hiring quality” actually mean?
Hiring quality is whether the people you hire do the job they were hired for, at the level you expected, and stay long enough for it to matter. That is measured months after the offer, not at it.
Useful measures include performance against the role’s outcomes at six and twelve months, retention at the same points, and the hiring manager’s view of whether they would make the same decision again. Earlier signals help too: the share of candidates who pass the final round, and how often panels disagree. Keep speed separate. Fast hiring and good hiring can coexist, but they are different measures, and improving one does not automatically improve the other.
Why does structure make such a difference?
Unstructured interviews let each interviewer test something different and weigh it differently, so the result says as much about the panel as the candidate. Structure reduces that noise. A 2022 re-analysis of earlier meta-analyses in the Journal of Applied Psychology found structured interviews to be the top-ranked of the selection procedures it examined. It also found that the predictive power of most methods had been overestimated in older studies [1].
The practical lesson is twofold. Use structure wherever you can, and do not assume any single method will predict performance well on its own. Combine methods, write down what you find, and check the results against what happens after the hire.
Five practices that improve quality
1. Turn every requisition into a scorecard
Before sourcing begins, the hiring manager and talent partner agree the four to six skills that matter most for this role, and what strong evidence of each looks like. For a machine learning engineer, that might be ML fundamentals, experience of shipping models to production, systems design, domain knowledge and communication. A scorecard written this way also improves the job advert and the sourcing brief.
2. Let people who do the work assess
Specialist skills are best judged by people who use them now. Give those interviewers a short guide: which skill each question tests, what a strong answer includes, and how to score it. Calibrate regularly by having two interviewers score the same conversation and discussing where they differ.
3. Set the pass mark before you see candidates
If the bar is set after the interviews, it drifts towards the best of whoever applied. Agree a threshold for each skill and overall before the first interview. If nobody passes, that is useful information: the role definition may be unrealistic, the compensation may be off, or the market may be thin for that profile.
4. Ask for written evidence, not impressions
Each interviewer should record what they asked, what the candidate said or did, which skill it shows, and the score, before the panel discusses the candidate. Writing first prevents the loudest voice from anchoring everyone else. Written evidence also helps a Site Lead explain a decision to global leadership months later.
5. Track what happens after the hire
At three and twelve months, compare each hire’s scorecard with how they are actually performing. Which skills predicted success? Which interview questions told you nothing? Feed the answers into the next version of the scorecard. This is the step most teams skip, and it is the one that makes the others improve over time.
What are the common symptoms, and their likely causes?
| Symptom | Likely cause | What to change |
|---|---|---|
| Hires pass interviews but struggle in the job | Interviews test recall rather than the work | Ask about systems the candidate has built and run; use problems that resemble the role |
| Panel members disagree widely | No shared definition of good | A scorecard with examples of strong answers, and regular calibration |
| Final rounds reject most candidates | Weak screening earlier in the funnel | Move a structured technical screen earlier, run by an engineer |
| Strong candidates withdraw | Too many rounds or slow feedback | Fewer, better-designed rounds and a fixed turnaround for decisions |
| Good hires leave early | Role turned out different from the description, or weak onboarding | Honest role outcomes in the brief; check-ins at 30, 60 and 90 days |
What about take-home tests and AI coding assistants?
Take-home tests have become harder to interpret now that AI coding assistants are widely used. Rather than banning the tools, a practical response is to ask candidates to walk through their own submission, explain the trade-offs, and extend it live. The aim is the same as in any assessment: evidence of judgement, not just a working answer.
How do you show leadership that quality is improving?
Report a small set of measures every quarter: final-round pass rate, panel agreement, retention at six and twelve months, and hiring-manager confidence. Add one or two short examples of hires whose scorecards matched their later performance, and one that did not, with what you changed as a result. Leadership tends to trust a process that shows its own corrections.
How Viamagus select approaches it
Our process follows the same principles. Each brief becomes a scorecard of five skills (ML fundamentals, shipped to production, industry fit, systems design and communication) with a pass mark of 75. Pass or fail is written down, and each candidate receives a dossier with a skill breakdown, role fit and a written verdict, signed off by a senior engineer. At one US-headquartered GCC, all three ML engineers we put forward cleared the client’s technical rounds. See how the assessment works or read the proof.
Sources
- Revisiting meta-analytic estimates of validity in personnel selection: addressing systematic overcorrection for restriction of range, Sackett, Zhang, Berry and Lievens, Journal of Applied Psychology 107(11), 2022