Hiring process templates

A security engineer hiring process built around a time-boxed, paid CTF-style challenge scored on a fixed rubric instead of certifications. **Why this works:** - A calibrated practical challenge measures the skill directly instead of trusting a certification as a proxy for it - The writeup counts as much as the flags: explaining a vulnerability, its impact, and the fix is the actual job - Independent rubric scoring makes every candidate comparable and the hiring decision auditable - Identity-verified references defend against deepfake candidates and fake-worker hiring fraud - Structured security assessment is core to Kit: the same platform runs bug bounty and vulnerability disclosure programs, so scoring security findings is what it does all day **Best for:** AppSec, product security, and detection engineering hires **Timeline:** ~2 weeks **Candidate effort:** 4-6 hours (paid)

A high-volume process for entry-level engineers built on one short, AI-allowed work sample instead of degree filters and long interview loops. **Why this works:** - Degrees and GPAs no longer certify competence; a short scored work sample predicts real ability far better - AI use is allowed and disclosed, so you score judgment and verification instead of playing detection - Deliberately few rounds: one assignment and one pairing interview, so decisions land inside a top candidate's 10-day window - The same task and rubric for every applicant keeps a high-volume junior funnel fair and comparable **Best for:** Junior and entry-level software engineers, internship-to-hire pipelines **Timeline:** ~1 week **Candidate effort:** ~2 hours (paid)

A lean hiring process for your first engineering hire, built around a paid work trial, an ownership interview, and a transparent founder equity conversation. **Why this works:** - A paid work trial on a real slice of your product is the single best predictor of on-the-job performance - The ownership interview screens for zero-to-one shipping under ambiguity, not algorithm puzzles - A dedicated equity conversation with plain numbers wins the candidates who understand risk and ownership - Deliberately few rounds: this hire is won on scope and trust, not process theater **Best for:** Pre-seed and seed startups making their first engineering hire **Timeline:** ~2 weeks **Candidate effort:** 6-10 hours (paid)

A behavioral hiring process for your first engineering manager, built on written management scenarios and structured leadership interviews instead of coding puzzles. **Why this works:** - Written scenario responses reveal real management judgment before you spend interview hours - Blind team review scores every candidate on the same leadership rubric, so the decision runs on evidence rather than the loudest voice - Two structured interviews separate people leadership from technical judgment without padding the loop - Skip-level references from engineers the candidate actually managed are the strongest signal on how they lead **Best for:** Startups making their first engineering management hire (teams of 6-8 engineers) **Timeline:** ~2-3 weeks **Candidate effort:** 2-3 hours

A reliability-focused process for DevOps and SRE hires that screens for incident judgment and real infrastructure work instead of certifications and trivia. **Why this works:** - Incident scenarios test the error-budget and troubleshooting reasoning that actually predicts on-call performance - A paid IaC and pipeline-debugging assignment shows operational skill, not resume keywords - Two focused interviews keep the loop short; strong infrastructure candidates drop off after round three - The on-call conversation is explicit, because rotation health decides whether the hire stays **Best for:** DevOps Engineers, SREs, and infrastructure-minded platform engineers **Timeline:** ~2 weeks **Candidate effort:** 5-7 hours (paid)

A backend hiring process built around a paid API and data-model take-home that candidates defend in a live systems interview. **Why this works:** - A scoped, paid take-home on a real scaffold shows how someone models data and designs APIs, not how they perform on a whiteboard - A hard time limit with auto-submit keeps the field level and respects candidates with jobs and families - Rubric-scored team review replaces gut-feel verdicts with specific, debatable observations - The live interview uses the candidate's own code as the agenda, the hardest signal to fake **Best for:** Startups hiring engineers to own APIs, data models, and production infrastructure **Timeline:** ~2 weeks **Candidate effort:** 3-4 hours (paid)

A structured process for AI and ML engineers built around a paid applied-ML take-home scored against an evaluation rubric. **Why this works:** - Portfolio and model links in the application surface shipped work before the first call - A paid take-home with its own eval suite separates engineers who ship from candidates who only talk fluently about AI - Rubric-scored team review keeps the decision anchored to the work, not to whoever spoke last in the debrief - The technical deep-dive probes the cost-latency-quality tradeoff that production AI systems live on **Best for:** Startups making their first AI/ML hire to ship features on foundation models **Timeline:** ~2 weeks **Candidate effort:** 4-6 hours (paid)

Comprehensive hiring process for mid-to-senior software engineers. Includes paid take-home assignment with team code review before live interviews. **Why this works:** - Code assignment reveals real coding ability (not whiteboard puzzles) - Async team review prevents groupthink - Two-stage interviews: technical depth + culture fit **Best for:** Backend, Frontend, Full-stack engineers **Timeline:** ~2 weeks **Candidate effort:** 5-8 hours (paid)