Hiring · July 19, 2026 · 9 min read

How to Hire a Software Engineer: A Practical Guide

How to hire a software engineer using a skills-first process: scope the role, test real work at the right level, assess AI fluency, and decide fairly and fast.

← Part of The five pillars of hiring: what assessments measure

If you are a hiring manager or talent leader trying to work out how to hire a software engineer, the stakes are unusually high: a mis-hire on a small team can stall a roadmap for a quarter, and the strongest candidates disappear into other offers within days. Yet engineering hiring runs on weak signal. Résumés are noisy, referrals are narrow, and the classic whiteboard puzzle mostly measures whether someone drilled the puzzle. A skills-first process fixes this by testing the work the role actually involves, at the level it actually needs, and by deciding on evidence rather than pedigree.

This guide walks through that process end to end, from scoping the role to a fair, fast decision. It is written for the person who owns the outcome, not the process for its own sake. Where a specialised role changes the picture, we point to the deeper role guides.

Why the usual approach mis-fires

Two failure modes dominate engineering hiring. The first is pedigree bias: filtering on employer names and degrees, which correlates weakly with who can actually do the work and quietly excludes self-taught and non-traditional engineers. The second is proxy testing: measuring something adjacent to the job, like memorised algorithm trivia, and hoping it predicts on-the-job performance. It rarely does. The most effective correction is to test the real work directly and to hold every candidate to the same standard, which is the core idea behind skills-based hiring.

There is a quieter third failure too: inconsistency. Even teams that test real work often let each interviewer improvise their own questions and their own bar, so two candidates for the same role are never measured against the same thing. That makes the pipeline impossible to compare, hard to defend, and easy for bias to slip into. The remedy is not more interviews; it is the same job-relevant assessment and the same rubric for everyone, applied in the same order. Standardisation is what turns a collection of opinions into a decision you can stand behind.

1. Scope the role before you screen

Decide what this engineer will really do and at what level before you touch a job board. A junior shipping well-scoped features is not a staff engineer setting architecture, and conflating the two produces a vague brief that attracts the wrong applicants and a rubric nobody can score consistently. Write the role down as outcomes: what should be true in six months if this hire goes well? That definition dictates everything downstream, from the assessment to the interview questions. Our guide on how to write a job description covers turning outcomes into a brief that filters for signal rather than keywords.

Generalist or specialist?

"Software engineer" spans very different jobs. If the role leans heavily in one direction, calibrate to that discipline: a backend engineer is judged on data modelling, APIs and reliability; a frontend engineer on interface architecture and performance; a DevOps engineer on infrastructure and delivery pipelines. Naming the specialisation up front stops you running a generic assessment that under-tests the part of the job that matters most.

2. Screen on skills, not pedigree

Put a short, job-relevant skills check before the full CV review rather than after it. Sequencing matters: a skills-first screen widens the funnel to capable engineers with unconventional backgrounds, and narrows it against the polished CV that hides a thin skill set. Keep this first check short and focused on one or two core competencies so it stays humane and completion stays high. The point is not to test everything at the gate; it is to replace a weak proxy, name recognition, with a small piece of real evidence.

This is also where volume becomes a problem worth solving. A popular opening can draw hundreds of applicants, and reading every CV by hand is both slow and inconsistent. An AI-generated assessment aligned to the specific role lets you evaluate everyone on the same job-relevant task at the top of the funnel, so the shortlist is built on evidence rather than on whose CV happened to catch a tired reader's eye. That reframes candidate evaluation from a sorting problem into a measurement problem, which is the version you can actually make fair.

3. Assess the real work, calibrated to level

Favour realistic tasks over abstract trivia. For most roles that means reading and fixing existing code, extending a small feature, or, for seniors, discussing a system design. Work samples predict on-the-job performance far better than brain-teasers because they measure the actual work, and they read as fairer to candidates because the relevance is obvious. Our guide to work sample tests covers how to design tasks that are realistic without being unpaid overtime.

What changes with seniority is not whether code appears but what the task asks. At entry level, predict-and-fix tasks and well-scoped feature work tell you the most. At senior level, critique-the-design and trade-off discussions matter more than raw throughput. Weight domain skill heavily for juniors and shift toward judgement for seniors, following the five-pillar model of cognitive, domain, situational, behavioural and AI fluency signals. The pillars keep you from over-indexing on one dimension and missing, say, an engineer who codes cleanly but cannot collaborate under ambiguity.

Take-home or live?

Both formats work when they mirror the real job and both fail when they test trivia. A bounded work sample gives you evidence you can probe later; a short structured live session shows reasoning in real time. Many teams run a live discussion on top of a work sample rather than choosing between them, which surfaces both the artefact and the thinking behind it. The failure to avoid is the open-ended, multi-day take-home: it rarely produces better signal than a bounded task, and it disproportionately screens out candidates with caring responsibilities or a current job, which is both unfair and self-defeating.

Beware the false negative

Most teams worry about hiring the wrong person. Fewer notice the cost of rejecting the right one. A puzzle-heavy process is engineered to produce false negatives: capable engineers who freeze on a contrived brain-teaser or who simply never drilled competitive-programming problems. Because you never see how those people would have performed on the job, the mistake is invisible and repeats itself. Realistic tasks, scored against a rubric, keep the bar high without turning it into a memory test, which is how you stop quietly discarding people who could have done the work well.

4. Assess how they work with AI

By 2026 most engineers work alongside AI coding tools daily. Whether a candidate uses them with judgement, verifying output, spotting a confidently wrong suggestion, and knowing when not to reach for the tool at all, is now part of the job rather than a bonus. An assessment that bans AI outright tests a version of the role that no longer exists; one that ignores AI misses a real differentiator between candidates. Treat AI fluency as a measurable competency, as covered in AI fluency as a hiring signal, and observe it in practice through an AI Sandbox session where the candidate works with tools on a realistic task. If the role is AI-heavy, such as a machine learning engineer, weight this pillar accordingly.

The distinction that matters is between using AI and using it well. Anyone can accept a generated suggestion; the engineer worth hiring reads it critically, tests the edge cases the model glossed over, and can explain why they kept or discarded it. That is a judgement skill, not a tooling skill, and it is best observed by watching the candidate work rather than by asking whether they "know" a given tool. A realistic session where AI is available, and where the interesting moments are the ones where the candidate overrides or corrects the tool, tells you far more than a checkbox on a CV ever could.

5. Interview to interpret evidence

The interview should not restart the evaluation from a blank page. Its best use is interpreting evidence the assessment already produced: walking through the candidate's work sample, probing their trade-offs, and asking what they would change. Keep it structured, with the same job-relevant questions and an anchored rubric for every candidate, so the interview measures the candidate rather than the interviewer's rapport. Our structured interviews guide sets out how to build and score one that holds up.

• Anchor the scale before you interview: agree what a strong, average and weak answer looks like so scores mean the same thing across interviewers.
• Score independently first, discuss second, so the loudest voice does not set the consensus.
• Probe the work, not the person: reasoning and trade-offs reveal more than culture-fit small talk.
• Keep the same core questions across candidates for the same role, and record scores against the rubric.

6. Keep it fair and fast

Score every candidate on the same rubric, keep each assessment humane in length to protect completion, and monitor outcomes across groups for adverse impact so a screening step is not quietly filtering out qualified people. Standardisation is what makes a decision both fairer and easier to defend if it is ever challenged. Speed matters just as much: strong engineers hold other offers, so a process that moves quickly, with clear decisions between stages and little dead time, wins more of them. The two goals reinforce each other, because a standardised process is also a faster one.

Communication is part of fairness as well. Tell candidates what each stage involves and roughly how long it will take, give them the outcome promptly, and keep the assessment relevant enough that even a rejected candidate feels it was a fair read of their ability. Engineers talk to each other; a process that respects their time becomes a quiet recruiting advantage, while a bloated, opaque one earns a reputation that shrinks your future funnel. The candidates you treat well but do not hire this time are often the ones who apply again, or refer someone, later.

Putting it together

Hiring a software engineer well is less about any single clever question and more about a coherent chain: scope the role as outcomes, screen on a short real task, assess the actual work calibrated to level, include how the candidate works with AI, interpret the evidence in a structured interview, and decide on the same standard for everyone, quickly. Each step removes a bit of noise the previous approach left in. The result is a process that surfaces capable engineers you would otherwise miss and stands up when someone asks how the decision was made.

Hire engineers on evidence, not résumés: test the real work at the right level, include how they work with AI, and keep the whole thing fair and fast.