Updated weekly — 200+ questions from 50+ AI companies

The standard forAI-era engineeringinterview prep

Practice on verified questions sourced weekly from engineer reports across Glassdoor, Blind, and Reddit. Scored on the exact production rubric top AI companies use in real interviews.

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200+
Problems
50+
AI companies
3-LLM
Tournament
6
Scoring dimensions
Questions verified from engineers at
Anthropic·OpenAI·Google DeepMind·Mistral·Cohere·Nvidia·Meta·Microsoft·Databricks·Scale AI·Hugging Face·Perplexity·TikTok·Waymo·Snowflake·Palantir·Uber·Airbnb·Amazon·Apple·Netflix·Anthropic·OpenAI·Google DeepMind·Mistral·Cohere·Nvidia·Meta·Microsoft·Databricks·Scale AI·Hugging Face·Perplexity·TikTok·Waymo·Snowflake·Palantir·Uber·Airbnb·Amazon·Apple·Netflix·

Built for the engineers getting hired

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The questions are really good. They are clear and concise, forcing you to think deeply, similar to a stressful interview or coding test. The 'golden truth' answers are much better than asking ChatGPT even with heavy steering.

Jun Hyun Bang · Pro user
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It's rare to see something that feels both technically sharp and thoughtfully designed at the same time.

Sree · AI Engineer
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As a Drexel student constantly applying across roles, from AI Engineer to Data Engineer, Velocode has been a useful addition to my interview prep routine. Even in my early uses, the production scoring opened my eyes to gaps I didn't know I had. It has been telling me if my code works; it breaks down exactly where you fell short on things like architecture and token efficiency. I also like the fact that it covers multiple tracks means I can pivot my prep depending on which interview is coming up next without switching platforms. Still early in my journey with it, but it's already changed how I think about preparing for technical interviews!

Jayan · Drexel University
★★★★★

Velocode quickly helped me identify my baseline AI skill level and provided a tailored roadmap for what to focus on next. As I worked through the different problem sets, the detailed scoring rubric pinpointed exactly where I needed improvement. By learning these concepts through Velocode, I felt much more confident articulating complex AI topics to both highly technical audiences and executive stakeholders in a clear, effective way.

Rahil Mehta · Senior AI Solutions Architect, Salesforce
★★★★★

Hands down the best platform for breaking into AI Security. Everything is built in, so you can go from learning prompt injection techniques to exploring RAG and Agentic AI security without spinning up a dedicated lab or thousands of dollars on certifications that barely scratch the surface of what's covered here.

Ayyappan Rajesh · Sr. Offensive Security Engineer
★★★★★

The question bank is really amazing. The exact questions being asked in interviews are there to practice, and most importantly the broken architecture questions. Token efficiency as a first-class dimension, not an afterthought — that's the gap no other platform closes. The 3-LLM golden answer tournament is a genuinely smart solve too. Single-model answers have a ceiling that cross-scoring breaks through.

Om Lakshkar · Applied AI Engineer, SwiftPitch / FOSSEE IIT Bombay
★★★★★

While most AI interview prep resources feel fragmented and noisy, Velocode stood out as my comprehensive, one-stop solution.

Utsav Agarwal
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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.

Ananya Kumar · AI Engineer
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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.

Jasmaine Khale · Backend Engineer, AI/ML systems

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AI Engineer

RAG systems, LLM apps, agents, vector databases, prompt engineering, fine-tuning, AI architecture

$150k-$220k

AI EngineerLLM EngineerGenerative AI Engineer
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ML Engineer

Model training, MLOps, deployment, monitoring, serving infrastructure, data pipelines, ML governance

$140k-$200k

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Data Engineer

SQL, ETL/ELT pipelines, Spark, dbt, Airflow, Snowflake, data modeling, streaming, data warehouses

$120k-$180k

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AI Security Engineer

Prompt injection, adversarial ML, model security, red-teaming, AI compliance, data privacy, governance

$150k-$210k

AI Security EngineerML Security EngineerAI Safety Engineer
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Software Engineer (AI-Era)

Modern software engineering is AI engineering. Google, Meta, and top tech companies have redesigned their hiring around one question: can you read, debug, and improve code using AI as a tool? This domain is built around that new format — real code comprehension problems from verified company interviews, with an embedded AI assistant and scoring across prompt quality, output validation, and debugging skill.

$140k-$220k

Software EngineerSenior Software EngineerFull-Stack Engineer
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PRODUCTION-GRADE SCORING

See exactly where you fell short

Three independent AI agents score your submission on the same dimensions used in real AI engineering interviews — not just whether tests pass.

✓ Token efficiency✓ Architecture✓ Security posture✓ Correctness

Then a 3-LLM golden answer tournament (Claude + GPT-4o) synthesises the definitive solution — better than any single model can produce.

Reported at OpenAI
Implement a RAG pipeline with citation tracking
71/100
Composite score
Correctness76/100 (G 95)
↑ +19 gap — your retrieval missed two case-law citations
Token Eff.34/100 (G 90)
↑ +56 gap — you passed too many chunks to the LLM
Architecture62/100 (G 88)
↑ +26 gap — single-pass retrieval lacks reranking
Security81/100 (G 92)
↑ +11 gap — good input validation, missing output PII scan
def rag_with_citations(query, docs):
    chunks = chunk_by_semantic_boundary(docs)
    embeddings = embed(chunks)
    top_k = retrieve(query, embeddings, k=5)
🔒 Company-tagged problems like this one — unlock with ProUpgrade to Pro →
General · Senior · 45-Min Session
Topics: RAG Architecture, Agent Systems
38:24
In your previous submission you approached RAG chunking with fixed-size windows. Can you walk me through your reasoning — specifically why you chose 512 tokens and what you'd change for a legal corpus with dense terminology?
I chose 512 tokens as a starting point based on typical embedding model context windows. For legal text I'd switch to semantic chunking — splitting on natural boundaries like paragraphs and clauses rather than token count.
SA
Good instinct on semantic boundaries. Now — if citation tracking is a hard requirement and two chunks reference the same underlying fact, how do you prevent duplicate citations in the LLM response?
Type your answer — or click mic for voice…
AI INTERVIEW SIMULATOR

The only simulator that knows your weak spots

Unlike generic AI mock interviews, Velocode reads your actual submission scores and builds a session targeting exactly where you scored lowest.

Scored 34/100 on token efficiency? The interviewer will probe that. Scored 81/100 on security? It moves on.

  • Voice mode — speak your answers, transcribed in real time
  • Staff-level AI interviewer — probes weaknesses, not strengths
  • Timestamped debrief — specific feedback on what you said and when
After session: Technical depth 6/10 · Communication 8/10 · Production awareness 4/10 · Sign in to practice on the simulator →

1 free session lifetime on Free; 3 sessions/month on Pro. Add-on sessions are $8.99 each or $25 for a 5-pack.

From zero to offer-ready

Four steps from your first submission to a production-ready answer.

01
Pick your domain
Choose from RAG Systems, AI Security, Agent Systems, MLOps, and more. Filter by company tag to focus on the exact questions reported from real interviews.
02
Solve the problem
Write code in our browser IDE. Run real test cases. For system design, draw your architecture on an interactive canvas.
03
Get production scores
Three independent scoring agents evaluate your solution across token efficiency, architecture, correctness, and security. See exactly where you lost points.
04
See the golden answer
A 3-LLM tournament synthesises the definitive solution. Study the senior engineer walkthrough. Understand the gap between your answer and the best possible answer.

Updated every week. Never stale.

Engineers report interview questions across multiple platforms each week. Our team verifies every one. Claude identifies individual questions, deduplicates against existing content, and classifies by domain before human review and publishing.

Reddit
r/MachineLearning · r/LLMDevs · r/cscareerquestions
Glassdoor
Company interview question sections · Company-attributed posts
Blind / TeamBlind
Verified professional interview reports · AI company threads
GitHub
Open-source AI engineering interview question repositories
Hacker News
Top interview threads scoring >50 upvotes
AI Engineering Blogs
Latent Space, Anthropic, OpenAI, DeepMind, and Hugging Face blog posts
Collected weekly
Every Monday across 6 platforms
Claude extracts
Questions identified, deduplicated, and classified by domain
Human reviewed
Every problem approved before publishing

Every approved problem goes through a 3-LLM golden answer tournament before publishing. No static, single-author solutions. The best answer wins.

🔄 Updated every Monday · Last run: Wed, May 20

Built for the new technical interview. Nothing generic.

Other platforms prep you for phone screens. Velocode preps you for the actual role.

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You're hereVelocode$19.99/mo$49.99/3moHackTheRounds$12/moLeetCode$35/moHello Interview$47/moInterviewDB$49/moExponent$79/mo
Problems across all five engineering tracksVelocode onlyAI Engineer · ML Engineer · Data Engineer · AI Security · SWE (AI-Era)
~~
Company-tagged questionsCurated from real interview reports
~
Production-grade scoringVelocode onlyNot just test pass/fail
Your score vs golden answerVelocode onlyDimension-by-dimension comparison
3-LLM tournament golden answerVelocode onlyClaude + GPT-4o cross-scored, best synthesised
Simulator targets your weak spotsVelocode onlyReads your submissions, probes weakest dimensions
Senior engineer walkthroughsAnnotated code, staff-level thinking
Updated weekly from live sourcesReddit, Glassdoor, Blind, GitHub, HN, blogs
~~~
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PRICING

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Free Plan

$0
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  • Full library of community AI engineering problems
  • Complete browser IDE — submit, run tests, see results
  • Full solution reveal on community problems (golden answer, senior walkthrough, score breakdown)
  • 1 interview simulator session (lifetime)
  • See all company-labelled problems (unlock with Pro)

No contracts. Cancel any time. All your progress and submissions are saved regardless of plan.

Frequently asked questions

What is Velocode AI?
Velocode is the only AI engineering interview prep platform that scores your submissions on token efficiency, architecture, and security — the same dimensions top AI companies use in real interviews.
How is it different from LeetCode?
LeetCode focuses on algorithmic puzzles. Velocode focuses on AI engineering specifically — RAG systems, agent orchestration, prompt injection, MLOps — and scores your submissions on production metrics, not just test-case pass/fail.
What roles does Velocode cover?
Velocode covers five tracks: AI Engineer (RAG, agents, LLMs), ML Engineer (model training, MLOps), Data Engineer (SQL, pipelines, Snowflake), AI Security Engineer, and Software Engineer (AI-Era) — the new code-comprehension format Google and Meta use to test whether you can read, debug, and improve code with AI. Each track has dedicated problems, scoring, and simulator sessions tailored to that role’s interview style.
What kind of questions are covered?
Velocode focuses on AI engineering interview questions across RAG systems, agent orchestration, prompt injection, MLOps, model serving, and AI security. Each problem is scored on production dimensions like token efficiency, architecture, and security.
Is there a free plan?
Yes — community AI engineering problems are completely free with full scoring and golden answer reveal. Company-tagged problems require Velocode Pro at $19.99/month.
How does the scoring work?
Three independent AI agents score your submission across token efficiency, architecture, security, and correctness. A 3-LLM tournament (Claude + GPT-4o, cross-scored) synthesises the definitive golden answer for comparison so you see exactly where you fell short.
What is the interview simulator?
The simulator reads your actual submission scores and runs a live interview targeting your weakest dimensions. Voice mode, staff-level AI interviewer, timestamped debrief. 1 free session lifetime on the Free plan; 3 sessions/month on Pro. Add-on packs available.
How often are new problems added?
New questions are collected and verified across Reddit, Glassdoor, Blind, GitHub, Hacker News, and AI engineering blogs every Monday. Each problem is extracted by Claude, deduplicated, classified by domain, and goes through a 3-LLM golden answer tournament before publishing.
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