Learn
Learn the concepts
34 lessons and 15 deep dives, 17 architecture patterns, and 14 technology breakdowns — plus the Delivery Framework for running the 45 minutes.
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Study the concepts, drill real interview questions, and run realistic AI mock interviews — each one scored with feedback that shows exactly what to fix and what to study next.
Free during beta — browse everything. Sign in only to grade attempts and track your skill profile.
Designed by FAANG engineers
What you get
Learn the concepts, practice real interview questions, and run full mock interviews. Every mode feeds the same evaluation engine and updates the same skill profile.
34 lessons and 15 deep dives, 17 architecture patterns, and 14 technology breakdowns — plus the Delivery Framework for running the 45 minutes.
Browse the library23 published problems with staged workspaces and diagrams. Solve at your own pace, then get reasoning-aware feedback when you are ready.
See problemsA conversational AI interviewer leads the round, pushes back, and asks follow-ups — then returns the same per-stage diagnostic debrief.
Try a mockHow it works
Learn, practice, and mock interviews all run the same loop — so each session turns into a specific, repairable next step instead of a vague score.
Attempt
Use structured workspaces for requirements, API, architecture, data, and scaling instead of staring at a blank page.
Diagnose
The evaluator explains where assumptions, estimates, trade-offs, data choices, or failure handling stopped supporting the design.
Study
Every miss maps to a skill lesson, deep dive, pattern, or reading path so the next study step is specific.
Retry
Retry the same stage or start a mock interview. Your skill profile updates across sessions.
A real debrief
The grader follows the reasoning chain behind your design — whether requirements constrained the architecture, whether estimates shaped capacity, whether trade-offs were defended, and whether failure modes were handled realistically.
Evaluation engine
Scope
what requirements were handled
Failure
what breaks under load
Retry
what to practise next
Assumption
Read traffic is much higher than writes, but the design never quantified the read/write ratio.
Trade-off
Redis and replicas were named, but staleness, invalidation, and failover ownership were not defended.
Robustness
The architecture has no answer for cache stampede or primary-region outage behavior.
Repair
Study read-heavy patterns, then retry the stage by stating numbers and consistency budget before drawing boxes.
Why it's different
Most AI graders keyword-match and praise. SystemRound grades against authored, per-stage rubrics, cites the evidence in your own answer, and ships adversarial checks locked to fixtures it must mark wrong — so the tempting-but-wrong answer fails here the way it fails in a real round.
Polite, encouraging, fooled by canon.
“We use a fixed-window counter — a simple INCRwith a 1-minute TTL per key. It's the simplest algorithm and resetting every minute is fine for our use case.”
Rate limiting · Passed
“Great choice — a fixed-window counter is simple, fast, and easy to reason about. You've got the core mechanism right. Keep it up!”
Specific, sourced, calibration-locked.
“We use a fixed-window counter — a simple INCRwith a 1-minute TTL per key. It's the simplest algorithm and resetting every minute is fine for our use case.”
Boundary burst · Missed
AdversarialA 100/min fixed window resets at the top of every minute. A client fires 100 requests at :59 and another 100 at :00 — 200 in two seconds, double the contractual limit. A sliding window or token bucket smooths the boundary.
Each adversarial check targets an answer that sounds right and gets praised elsewhere — the exact traps strong candidates still walk into.
The miss cites the line in your own answer that triggered it, with the first-principles reason it fails under load.
Every trap ships with a fixture the grader must mark wrong. If a model change starts praising it, our build fails before it reaches you.
Who this is for
From your first design interview to a staff-level loop, SystemRound meets you where you are — and tells you the truth about where you stand.
Your first design rounds, or stepping up from coding interviews. Learn the structure and vocabulary, then drill it until the blank "design X" page stops being scary.
Rounds where depth and trade-offs decide the offer. Practice defending capacity math, consistency choices, and failure modes — not naming boxes.
Rebuild the system design muscle fast. A repeatable attempt → diagnosis → study → retry loop replaces re-reading the same blog posts.
The library
When the debrief finds a gap, it points into real depth — not a one-line tip. Authored lessons, patterns, and technology deep dives carry the same rigor as the grader.
Browse the libraryHow to run the 45 minutes — the six phases, time budgets, and what to say in each.
Graded skills plus 15 deep dives, each with worked answers, decision tables, and an interview playbook.
The system shapes interviewers expect you to recognise on sight, with scaling paths and failure modes.
Deep dives on Redis, Postgres, Kafka, DynamoDB and more — internals, real numbers, and production case studies.
Graded skills
Published problems
Architecture patterns
Reading paths
FAQ
Yes — every published problem, lesson, and mock interview is free during beta. No credit card. A paid tier will only land once it adds real value beyond what is here today.
A general model keyword-matches and tends to praise. SystemRound grades against authored, per-stage rubrics, cites evidence from your own answer, and ships adversarial checks locked to fixtures the grader must mark wrong — so the tempting-but-wrong answer fails here the way it fails in a real round.
Every problem has authored criteria and calibration fixtures the evaluation must pass on each build. The AI is an evaluation layer on top of those rubrics, not a freeform opinion — and any judgment can be disputed and re-checked.
No. Browse problems, lessons, and the whole library freely. Sign in only when you want to grade an attempt, save it, and build your skill profile across sessions.
Built for mid, senior, and staff system design rounds. The library spans 19 graded skills, 17 architecture patterns, 14 technology deep dives, and the Delivery Framework — across 23 end-to-end problems.
The rubrics, lessons, patterns, technology deep dives, and problem walkthroughs are all authored by engineers who have run and sat these interviews. The AI applies that authored judgment to your specific answer.
All published problems are free during beta. Browse freely; sign in when you are ready to grade and keep your profile.