AI engineering looks like software engineering. It isn't. Production RAG, agent orchestration, prompt injection, eval pipelines — none of it shows up in a LeetCode-style screen. Velocode tests what production AI engineering actually requires.
A Series B startup spent six months recruiting a Senior AI/ML Engineer from a FAANG company. Three weeks in, the CTO asked them to implement a basic data pipeline. The engineer couldn't do it. Not "did it poorly" — literally couldn't write the code.Read the full article →
The average fill time for AI roles dropped to 25 days because smart companies have figured out that a drawn-out process is the same as a rejection. If your interview timeline stretches past three weeks, you are almost certainly losing people to someone who moved faster.Read the full article →
We removed our coding and algorithms interviews and replaced them with an AI-native onsite. The role is shifting from building the machine to designing and honing it.Read the full article →
These aren't outlier stories — they're the pattern. AI engineering is a genuinely different discipline. Three-quarters of AI engineering interview questions now revolve around RAG, agents, and LLM evaluation (DataCamp, 2026). Generic SWE assessments don't catch the gap. Velocode is built for that gap.
No question library to browse. No leetcode. Just paste your job description; we generate an assessment that tests what the role actually requires.
Our AI extracts the seniority, domain, and skills automatically. Or pick from 300+ verified questions reported from real interviews at top AI companies.
Custom-built around your role in under 60 seconds. Preview, swap, or refine any problem. Each one carries a verified golden answer from our 3-LLM tournament.
Candidates get a clean, branded assessment. They can use Claude, Cursor, Codex — whatever they'd use on the job. Everything is tracked, nothing is banned.
Each candidate scored across 4 production dimensions: correctness, architecture, efficiency, and security. Compare directly against the golden answer. You see the gap, not just a number.
Every submission is graded on what AI engineering teams actually evaluate — not pass/fail test cases.
Does the solution work? Does it correctly handle the constraints in the question — latency budgets, accuracy targets, edge cases?
Is the system design appropriate for the problem? Are layers separated, service boundaries clean, scaling concerns addressed?
Does the solution minimize unnecessary LLM calls, oversized contexts, and wasteful retries? (Renders as “Production efficiency” for non-coding domains.)
Are prompt injection, data exposure, and authentication boundaries handled? Is sensitive data sanitized before leaving the system?
Velocode runs the largest library of AI engineering interview questions on the internet. Engineers prepping for AI roles study on the exact same scoring dimensions you're assessing them on — meaning scores correlate with real interview readiness, not test-taking luck. No other assessment platform has this. See our full question library →
Banning AI tools doesn't reflect the job. We score what AI can't fake — architectural reasoning, production tradeoffs, and security thinking. The best AI engineers use AI tools constantly. We watch how they use them.
Strong AI engineers know within 30 seconds whether a screen tests their actual work. Velocode questions are reported from real interviews — they recognize the rigor and they engage.
Candidates receive the link from you — your email, your relationship. We never contact your candidates directly.
Most assessment platforms are general SWE tools with AI bolted on. Velocode is built ground-up for AI engineering hiring.
| Feature | Velocode | CodeSignal | HackerRank | Codility | Rounds.so | Saffron | Litmus |
|---|---|---|---|---|---|---|---|
| Built exclusively for AI engineering | ✓ | — | — | — | — | — | — |
| Production scoring across 4 dimensions | ✓ | ~ | — | — | — | — | — |
| Same library candidates prep on | ✓ | — | — | — | — | — | — |
| 5 specialized engineering domains (AI Eng, ML Eng, Data Eng, AI Security, SWE AI-Era) | ✓ | — | — | — | — | — | — |
| AI tool use allowed and scored | ✓ | ✓ | — | ~ | ✓ | ✓ | ✓ |
| Real-codebase / repo-grounded assessments | — | — | — | — | — | ✓ | ✓ |
| Anti-cheat without banning AI | ✓ | ~ | — | ~ | ~ | ~ | ~ |
| Self-serve, transparent pricing | ✓ | — | — | — | — | — | — |
| Custom assessment from job description in <2 min | ✓ | ✓ | — | — | — | — | ✓ |
Comparison reflects publicly stated capabilities as of May 2026. Each platform has strengths in its target market — we built Velocode specifically for AI engineering hiring.
Most AI hiring happens in bursts — open a role, screen 30–80 candidates, hire, close. Subscriptions don't fit that pattern. Pay per role you're actively hiring for, or subscribe if you're hiring continuously.
All tiers include the full scoring rubric, anti-cheat signals, golden answer comparison, and access to all 5 engineering domains.
For teams hiring 10+ AI engineers per year. Annual contracts. White-label deployment, dedicated solutions engineer, custom integrations.
Compare per-completion: Velocode Single Role $17.98 · HackerRank Pro ~$15 · Codility Scale ~$20. Most platforms charge less per completion but lock you into a $4,490+ annual commitment. We don't.
No setup fees. No per-seat upcharges within your plan. One-time packs expire after the stated window — unused completions don't refund. Cancel subscriptions anytime (prorated).
See a live demo of the platform, talk through your hiring loop, and we'll have a custom assessment ready for your role within 48 hours.
Most demos happen the same week. We'll come prepared with a tailored assessment for your role.