Send a link. Your candidate completes a real engineering problem — AI engineer, software engineer, ML engineer, data engineer, or security engineer — on their own time. You see ranked results, scores, and a hiring recommendation in 20 seconds. No live sessions. No scheduling. No guessing from a resume.
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. Engineering work has changed. Resumes and algorithm puzzles don't show whether someone can actually build, debug, and ship. Velocode does.
Paste your job description. We build the assessment. You send one link. Results come back ranked, with scores and recommendations.
Or pick a role. We match the right problems from 300+ verified questions taken from real interviews.
Custom-built around your role in under 60 seconds. Swap any problem. Cross-scored against multiple AI models.
Candidates complete it on their own time. AI tools allowed. Everything is tracked, nothing is banned.
Every candidate gets a score and a hiring recommendation. See exactly what they understood — and where they fell short.
Banning AI tools doesn't reflect the job anymore. We score the parts AI can't fake — how candidates think through tradeoffs, design real systems, and reason about security. The best engineers use AI tools constantly — we just watch how they use them so you know if they actually solved it or just pasted an answer.
Every domain is tested on real work — what the role actually requires on day one, not algorithm puzzles.
Build, debug, and evaluate AI systems in production. Real problems from real AI interviews.
The AI-era format Google and Meta now use — AI-assisted debug and code comprehension, not memorization.
Deploy and maintain models in production. Pipeline failures, monitoring, real scenarios.
Real pipeline work — dbt models, Airflow debug, Spark failures. Not SQL puzzles.
Defend AI systems against real attacks — prompt injection, jailbreaks, guardrails.
Know in 20 seconds whether this candidate can actually do the job.
No more 45-minute live sessions that tell you nothing.
No more making a $200K decision based on a LeetCode score.
Every submission is scored on what hiring teams actually care about — not pass/fail test cases.
Does the solution actually work for the real constraints — not just the happy path?
Is the system design sensible? Clean boundaries, right scale, right tradeoffs.
No wasted work. No unnecessary AI calls. No oversized inputs that burn money.
Are the obvious attacks handled? Prompt injection, data leaks, missing auth.
The rubric adapts to the role — ML engineering assessments weight deployment and monitoring; data engineering assessments weight pipeline reliability and SQL depth; security assessments weight attack defense.
Good engineers know in 30 seconds whether a screen actually tests the job. Velocode problems come from real interviews at working engineering teams.
Candidates receive the link from you — your email, your relationship. We never contact them directly.
def search(query: str, k: int = 10):
# Stage 1 — embed the query
q_emb = embed_text(query, model="text-embedding-3")
# Stage 2 — vector search over the corpus
docs = vector_db.search(q_emb, k=k)
# Stage 3 — filter weak matches
return [d for d in docs if d.score > 0.7]
Most platforms test algorithm puzzles and call it engineering hiring. Velocode tests the actual work — across every engineering role.
| Feature | Velocode | CodeSignal | HackerRank | Litmus | Saffron |
|---|---|---|---|---|---|
| Built for modern engineering hiring (not algorithm puzzles) | ✓ | — | — | — | — |
| Scores what actually matters: correctness, design, efficiency, security | ✓ | ~ | — | — | — |
| Candidates already prep on the same scoring | ✓ | — | — | — | — |
| Covers all 5 engineering roles (AI / SWE / ML / Data / Security) | ✓ | — | — | — | — |
| AI tools allowed and scored | ✓ | ✓ | — | ✓ | ✓ |
| Tests on a real codebase, not toy problems | — | — | — | ✓ | ✓ |
| Cheating caught without banning AI tools | ✓ | ~ | — | ~ | ~ |
| Custom assessment from a job description in under 2 minutes | ✓ | ✓ | — | ✓ | — |
Comparison reflects publicly stated capabilities as of May 2026. Each platform has strengths in its market — we built Velocode specifically for modern engineering hiring.
“While most AI interview prep resources feel fragmented and noisy, Velocode stood out as my comprehensive, one-stop solution.”
“The question bank is genuinely impressive. The exact questions being asked in interviews are there — and the way it scores how you use AI is unlike anything else out there.”
“Velocode is the only platform I found that actually tests what engineering interviews are testing now.”
“Most platforms just tell you if your answer was right. Velocode scores how you actually think, how you use AI tools, whether you caught the failure modes, your reasoning. It felt way closer to a real interview signal than a simple right/wrong.”
“Velocode fills a gap that no other platform does — it's not just about solving the problem, it's about reasoning through it under pressure, and that's exactly what real AI interviews test. I finally feel like I'm preparing for the right thing.”
Pick a time below. We'll build a live assessment for your open role on the call — no prep needed from you.