The Standard for AI Engineering.
Master the stack with real-world practice or hire top talent using automated technical screens.
For individuals
Learn while you code
Level up for real interviews—practice that feels like the job, not a trivia app.
- Real interview scenarios: Solve the same kinds of problems asked at top AI labs like OpenAI and Anthropic.
- Live mentor feedback: Instant senior-peer reviews as you code. Our AI mentor catches logic gaps, security risks, and high-cost patterns in real time.
- Interactive learning modules: Master the theory behind RAG, Agents, and AI security through hands-on modules built for production—not just syntax.
For companies
Automate your technical screen
Work trials that mirror real stacks—scored on economics, latency, and reliability—so your staff engineers trust the signal and you ship candidates who can own production AI.
- Automate your technical screen: Stop manual code reviews. Deploy work trials that mirror your actual stack—scored on economics, latency, and reliability.
- Role-Specific Simulations: Move beyond generic LeetCode. Test for specific expertise in RAG Architecture, Agent Tool-Use, or AI Guardrails.
- Production-grade scoring: Every submission is automatically audited for token efficiency, p95 latency, and security posture—with quantitative scorecards you can stand behind in exec reviews.
- Cheat-resistant validation: Low-signal patterns surface early; written architectural justification ensures you only interview candidates who truly understand the “why.”
Trusted by engineers from industry-leading AI teams
Names below indicate where members of our community work — not company endorsements or paid partnerships.
Coverage
The six pillars of AI-native engineering
Everything we teach and assess rolls up to these domains—so your profile and pipeline stay comparable. Tap a pillar for what it covers.
AI Engineering (Core LLM & RAG Logic)
Designing prompts, retrieval, chunking, and inference paths that ship in production—balancing quality, cost, and latency when models behave non-deterministically.
AI Security (Red-Teaming & Safety)
Threat modeling for LLM systems: jailbreaks, prompt injection, data leakage, guardrails, and validation so AI features do not widen your attack surface.
AI Systems Architect (Orchestration at Scale)
Multi-service design—routing, caching, streaming, and fault isolation—so AI workloads stay coherent as traffic, teams, and integrations grow.
MLOps & Infra (Deployment & Performance)
Shipping and operating AI in the real world: CI/CD for models and prompts, observability, benchmarking, hardware and vector infra, and sustainable performance.
Agent Systems (Autonomous Tool-Use)
Tool-calling, planning, memory, retries, and boundaries when agents use APIs and external systems—with clear ownership when something fails.
Data & Governance (Compliance & Quality)
Dataset hygiene, PII handling, lineage, policy alignment, and quality gates so AI products meet the bar for trust, audits, and regulated environments.
For hiring teams
Hiring with signal
Built for CTOs who are tired of "prompt engineers" who can't ship production systems.
Automated technical screens
Replace the manual first round with a 30-minute simulation of the actual job.
Scored on production metrics
We don't just check if the code works. We score candidates on Cost (token waste), Speed (latency), and Safety (guardrails).
Trust & integrity
Defensible screening—built for real work samples
Submissions are judged against structured rubrics tied to production-style scenarios—not keyword checks or multiple-choice trivia. Candidates explain trade-offs in writing so shallow or copy-pasted answers are easy to spot before they consume your staff's time.
- Comparable scores: the same bar and artifacts across your pipeline, so hiring managers can compare candidates without re-inventing the screen each time.
- Written justification: architecture and economics have to match the code—prompt-only "solutions" wash out.
- Efficiency & session signals: token waste, latency, and behavioral cues (e.g., paste timing) flag low-signal submissions before they clog your loop.
About Velocode
Built by senior engineers who saw the AI hiring trust gap firsthand.
Traditional LeetCode doesn't measure an engineer's ability to orchestrate non-deterministic systems. We built Velocode to standardize how the industry audits AI engineering competency—work samples in sandboxes, architectural scrutiny, and economic signals (tokens, latency, risk) execs can stand behind.
Our mission is to move the industry from “prompting” to production architecture. We help individuals prove their worth and companies de-risk their most expensive hires.
Production audit
Beyond Syntax: Automated Production Audits.
Stop manual code reviews. Automated audits surface architectural efficiency, token optimization, and security risks—with quantitative scorecards you can compare across your pipeline.
Candidate scorecard
Anonymous · RAG take-home
Submitted 2h ago · Python
Economic impact · token waste (est.)
$12.40/mo
at 1M reqs/mo vs. optimized benchmark
System reliability · latency delta (p95)
+15%
vs. golden reference trace
Overall signal
Strong hire
82nd percentile vs. calibrated baseline
Security posture
1 finding
Low — unsanitized doc path (remediated in review)
Audit summary
Architectural efficiency: good chunking strategy; tighten hot-path caching. Token optimization: embedding batch size suboptimal—~8% excess spend. Output quality aligned with rubric expectations on cited facts.
How it works
Real problems. Real execution. Real feedback.
The same pattern engineers use in production—modeled as challenges you can run, not just read about.
Rubric-grounded review
Each challenge ships with clear success criteria. Your work is reviewed against those expectations—so scores reflect engineering judgment on real constraints, not vibes or trivia.
Forensic 10-K audit
Implement a RAG pipeline over a 10-K: extract risk factors with citations. Return structured JSON with section, summary, and quote. Optimize for retrieval quality and latency p95.
Senior-style mentor review
Review: Your retrieval path shows p95 latency around 800ms—consider a lighter embedding pass or cache warming for hot sections. Document overlap is slightly high; tightening chunk boundaries could improve precision on long filings.
Same structured critique hiring teams see—grounded in production practice, not vanity metrics.
Build Calibrated Technical Screens in 30 Seconds.
Choose from 6 domains and 50+ technical tracks (RAG, Agents, MLOps, and more). Calibrate for Junior, Mid, or Senior roles so every screen matches the economic impact you need from the hire.
Domains
Seniority
Tracks (50+)
Illustrative UI — enterprise pilots include workflow and ATS integrations tailored to your process.
Velocode for teams
Enterprise hiring, calibrated to production AI engineering
Technical screens, work-sample audits, and scorecards your staff engineers trust—so the first screen your stakeholders see feels intentional, not empty.
The Hiring Standard for Production-Grade AI Engineers.
Stop manual code reviews. Automate your technical screens with custom-built work samples and deep architectural audits.
Calibrated screen · three steps
Select domain
- AI Engineering (Core LLM & RAG Logic)
- AI Security (Red-Teaming & Safety)
- AI Systems Architect (Orchestration at Scale)
- MLOps & Infra (Deployment & Performance)
- Agent Systems (Autonomous Tool-Use)
- Data & Governance (Compliance & Quality)
Anchor the screen to the role archetype your org actually hires for—one of six AI-native domains.
Select seniority
Junior · Mid · Senior
Calibrate difficulty to the compensation band and scope of ownership.
Select tracks
Tool-calling · RAG latency · PII filtering
Depth on real stack primitives — not generic trivia or LeetCode clones.
Calibrated Assessment Builder
Don't settle for generic quizzes. Select from 6 domains and 50+ specialized tracks—from Multi-Agent Orchestration to PII Filtering. Calibrate the difficulty for Junior, Mid, or Senior roles to ensure the challenge matches the salary.
Automated Production Audits
Every submission goes beyond syntax checks. Get a one-page audit on:
- Economic Impact: Estimated token waste vs. optimized benchmarks.
- System Reliability: Latency p95 projections and error-handling maturity.
- Security Risk: Real-time detection of prompt injections and leaked secrets.
Post-Hire Onboarding
Use the same audit data to fast-track onboarding. Identify exactly where your new hire needs support—whether it's Vector DB indexing or Context Window management—before they push their first PR.
Prefer email? We'll follow up with next steps for your team.
Playground
Sandboxed challenges from real AI engineering interviews—run code, see results, iterate fast.
Open PlaygroundLearning tracks
Domain → track → module → unit paths with theory, video, and hands-on practice.
Browse tracksInterview simulator
Pro mock sessions with a staff-level interviewer focused on systems thinking and tradeoffs.
Coming soonLearning domains
North-star paths for AI Engineer, Security, MLOps, and more—structured for depth, not just buzzwords.
Featured courses
View all tracksChallenge difficulty
Entry-level → AI Engineer 2 → Senior
Ready to build the future of AI?
Start in the playground, or book a pilot for automated production audits and calibrated hiring screens.