# Hiring for software engineering

Intervieux runs every software engineering applicant through a live AI interview that includes a real technical component, coding, debugging, or SQL, with code that actually executes and gets checked against test cases, then lets screening rules sort candidates by score before anyone on your team opens a resume.

A GitHub link on a resume tells you someone has a GitHub account. A take-home that sat in someone's inbox for two weeks and came back with an obvious ghostwritten commit history tells you nothing you can act on. Engineering hiring has a specific volume problem: strong signal is expensive to produce and easy to fake, and most of the funnel gets spent finding out which resumes were worth a second look.

## What screening engineers is actually like

Resume keywords do not distinguish a candidate who shipped production code under real constraints from one who padded a project list with tutorial repos. A phone screen catches obvious gaps but burns thirty minutes of an engineer's time per candidate, and at any real applicant volume that time runs out fast. Take-home assignments solve the depth problem but introduce a new one: response rates drop once a candidate realizes the assignment will cost them an evening, and the ones who do submit sometimes submit someone else's work. None of that is a people problem. It is a sequencing problem: technical signal usually arrives too late in the funnel, after a recruiter has already spent time on candidates who were never going to clear a real bar. By the time an engineer finally sits across from a candidate, the question that mattered most, whether this person can actually write code that runs, still has not been answered by anything in the file.

A junior opening and a staff opening are not screened the same way either. A junior candidate's coding challenge can test fundamentals with room for hints. A staff candidate's interview should probe how they reason about tradeoffs under ambiguity, which a fixed test alone will not surface. Treating every engineering requisition with the same rubric is how a hiring team ends up either underqualifying senior candidates or overwhelming junior ones before either gets a fair look.

## How screening runs for an engineering role

1. **Create the req or import a posting you already have** — Paste in a job description you already have and AI job setup drafts the scoring traits, a rubric, and interview questions for that specific role, editable before anything goes live. There is no separate rubric to build from a blank page for a backend versus a frontend versus a platform opening.
2. **Candidates apply into a live AI interview with a real technical component** — A candidate who applies gets a screening interview run against your exact configuration, and for a technical role that can include a coding, debugging, or SQL challenge with code that actually executes on real language runtimes and gets checked against hidden and visible test cases, not a submission that only gets read.
3. **Screening rules sort the pipeline the moment scoring finishes** — As each interview completes, your rules evaluate automatically against the technical score, the challenge score, experience level, key skills, and the rest of the scored fields, tagging or moving strong candidates forward and filtering weak ones out before a human opens the pipeline.
4. **Pull a ranked shortlist** — Generate a top-N shortlist by weighted score across the five interview dimensions whenever a role has enough completed interviews to make ranking useful, ready to hand to a hiring manager or an engineering lead for the next round.
5. **Schedule, offer, and sign without leaving the pipeline** — Send a self-service booking link for the human technical round, extend an offer with a public accept or decline page, and get a contract signed, all inside the same system the interview scores already live in.

## A screening rule built for an engineering funnel

A rule for a backend opening might read: technical score at or above 7, AND challenge score at or above 6, AND experience level at least mid, tag the candidate fast-track and notify the hiring manager. A second rule can run in parallel: technical score below 4 OR challenge score below 3, set stage to rejected. A third can catch a specific pattern a team keeps seeing, strong technical score AND salary estimate above the posted range, flag for a compensation conversation before scheduling anything. All three fire automatically the instant a candidate's interview is scored, so a recruiter opens the pipeline to a list already split into who is worth a look and who is not, instead of forty untouched cards.

## Technical challenges that match the actual role

A coding challenge for a frontend opening and a SQL challenge for a data-adjacent backend role are not the same test, and they should not be. Custom challenge authoring lets you write a problem specific to the role, coding, debugging an existing snippet, or a SQL query task, with AI drafting a first version of the problem and a suggested answer key, hidden and visible test cases, and a difficulty level matched to seniority. Submitted code runs for real on the execution engine and gets checked against those test cases, so a candidate is scored on whether their solution actually works, not on how confidently they described it.

## Finding engineers who never applied

Semantic talent search reads a plain-language description instead of exact keyword filters. A recruiter can search My talent pool, or engineers already in past pipelines, with something like a backend engineer who has scaled a service under real production load and is comfortable pairing on system design, and get candidates whose actual interview evidence matches that description even if their resume never used those exact words. A different query, a candidate who has debugged a hard concurrency bug and can explain the fix clearly, pulls a different slice of the same pool. Switching the search to the Intervieux network extends either query to candidates elsewhere on the platform who opted into being discoverable.

## An API your own engineers can use

The ATS runs on a full REST API with OpenAPI documentation, and a hosted remote MCP server that plugs directly into Claude, ChatGPT, or a custom tool with proper OAuth and consent screens. An engineering team evaluating a hiring tool can pull pipeline data, trigger rules, or query candidates the same way they would query any other internal API, instead of trusting a dashboard alone.

## Frequently asked questions

### Does the AI interview actually test coding ability, or just talk about it?

For a technical role, the interview can include a real coding, debugging, or SQL challenge. Code submissions run on an actual execution engine and get checked against test cases, so scoring is based on whether the code works, not on how the candidate describes their experience verbally.

### Can I write a technical challenge specific to our stack?

Yes. Custom technical challenges let you author a coding, debugging, SQL, or written problem for the role, with AI drafting a starting problem and answer key, and a test-run available before candidates ever see it.

### How do screening rules handle a high-volume engineering funnel?

Rules evaluate automatically the moment each interview is scored, checking technical score, challenge score, experience level, key skills, salary estimates, and strengths or weaknesses. A rule can tag, move stage, flag, prioritize, or notify a teammate the instant a candidate qualifies, without anyone opening the pipeline manually.

### Does Intervieux connect to Greenhouse, Lever, or Workday?

No native connectors to those systems exist today. Jobs can be posted directly or brought in through the job board's own import and feed pipeline, and candidate data lives inside Intervieux's own ATS rather than syncing with an external ATS.

### Can our engineers pull candidate data into our own tools?

Yes. A full REST API with OpenAPI documentation and a hosted remote MCP server are available, so pipeline data, rules, and talent search can be reached from Claude, ChatGPT, or custom internal tooling with proper OAuth-based consent.

## Related pages

- [Custom technical challenge authoring](/features/custom-technical-challenges)
- [Technical AI interviews](/features/technical-interviews)
- [Automated screening rules engine](/features/screening-rules)
- [The ATS REST API and hosted MCP server](/features/ats-api)
- [Compare Intervieux to Karat](/compare/intervieux-vs-karat)
- [Browse open roles](/jobs)

## Screen your next engineering hire with real code

Set up a role, drop in a technical challenge, and let screening rules sort the pipeline before you spend a single hour reading resumes.

Start practicing free: https://www.intervieux.ai/register · Hire with Intervieux: https://www.intervieux.ai/employers/signup