Back when I was preparing for my first Big Tech interview, I prepped the way most engineers do: reviewing concepts, solving hundreds of LeetCode problems, and watching every System Design video I could find. After months of grinding, I thought I was ready.
For a final check, I set up a mock interview with a friend who had just joined Microsoft. It went well enough. I solved the algorithm, explained my approach, and wrapped up on time. But then I asked them a simple question: “Did I justify my decisions well enough?”
They gave me a generic answer and moved on. The feedback wasn’t wrong, but it wasn’t useful either. I walked away with more questions than answers. That’s when I realized the gap in my prep: I could solve problems, but I had no way to measure how I came across in the conversation. The human-to-human interaction was where my prep fell short.
That gap stayed with me, and years later, it became the spark for what is now mockinterviews.dev, an AI-powered MAANG mock interview platform designed to give engineers the kind of lifelike practice I wished I had back then.
What makes realistic mock interviews essential for MAANG prep?
Fast-forward a few years to my time at Meta and Microsoft. I experienced the same thing again while interviewing candidates for engineering roles across levels. Many looked prepared on paper, but they struggled once the interview turned into a live conversation.
Some froze the moment I interrupted their solution. Others got tangled when I pressed on trade-offs. A few talked in circles, running out of time without clearly making their point. What I saw wasn’t a knowledge gap, but a practice gap. They had trained for drills—not for live interviews.
The usual prep options don’t fix this. LeetCode, YouTube, Reddit threads, and expensive coaching sessions are fragmented. None of them mirrors the flow and pressure of a 60-minute MAANG interview. And if practice doesn’t feel real, it won’t prepare you for the interview.
That was the problem I couldn’t shake: practice felt controlled and predictable, but the real interview was messy, conversational, and high-pressure.
How AI fixes common mock interview problems
At Educative, we tried solving this problem with peer-to-peer mocks. The idea was simple: connect candidates with experienced engineers. In theory, it worked. In practice, it fell apart.
We ran hundreds of sessions, and nearly half were rescheduled or canceled at the last minute. The experience varied significantly: some coaches left one-line comments like “good problem-solving,” while others provided multi-page feedback. The inconsistency made it impossible to standardize quality; without consistency, the model couldn’t scale.
Meanwhile, one insight stuck with me: mock interviews shouldn’t be a privilege. They should be a regular habit, accessible to any engineer. But peer-to-peer formats couldn’t scale.
When AI tools started to mature, the idea came back into focus. What if a browser tab could act like a seasoned MAANG interviewer, handling coding, design, and behavioral rounds, available anytime to anyone who wanted to practice?
The goal wasn’t to replace humans. It was to make consistent, realistic practice possible at scale. The prototype showed how far we had to go. It couldn’t run code and handle live prompts simultaneously and crashed almost every session. Fixing that single bug showed the idea could work, combining coding and conversation in one flow. Each fix led to another step toward building something engineers could use.
How our AI mock interview agent works
The goal was simple from the beginning: practice should feel like a real interview. That meant simulating the same combination of coding, design, behavioral conversations, time pressure, and feedback that candidates come across in the real MAANG loop.
To get there, we started with the core components:
- Coding widget with execution and live prompts, making problem-solving feel like a real coding round.
- Diagramming tool for System Design and object-oriented interviews, paired with conversational follow-ups.
- Dynamic behavioral interviews where answers trigger deeper, tailored follow-ups.
- Voice support to make interviews conversational and natural.
- Structured feedback, modeled after real interviewer debriefs, with ratings, examples, and actionable next steps.
These pieces formed the foundation, and even at this stage, the agent felt more like a real interview than anything I had tried earlier. But this wasn’t enough. The goal was clear for many engineers: MAANG interviews with their own distinct pace, culture, and expectations.
The next step was to create tracks tailored to each company.
Inside MAANG+ interviews
General realism is the foundation. But many engineers want practice that mirrors the exact experience at MAANG companies. That’s why on mockinterviews.dev, we built dedicated interview tracks for Microsoft, Amazon, Apple, Meta, Google, LinkedIn, Netflix, and Oracle, with more on the way.
These aren’t just generic question banks. Each track is tailored to match the unique style of these companies, tone, pacing, strictness to follow-ups, and even the way feedback is delivered were modeled on how interviews at these companies really run.
Coding interviews
Every company approaches coding interviews differently. At Apple, questions often focus on algorithmic efficiency and optimal solutions under time pressure. Google emphasizes edge cases and deeper complexity analysis. Meta values structured reasoning and clarity of approach. Microsoft is known for pushing candidates to explain trade-offs and justify design choices during coding rounds.
On our platform, coding tracks reflect these differences:
Apple-style coding: Short, focused problems with strict expectations on optimization.
Google-style coding: Multiple follow-ups exploring edge cases, with increasing difficulty levels if you handle basics well.
Meta/Microsoft coding: Conversational prompts that require explaining “why” as much as the “what,” with interruptions to test reasoning.
Oracle/LinkedIn/Netflix coding: Vary between many small problems and one long, evolving problem.
The code widget plus live conversation makes the experience feel less like LeetCode practice and more like adapting under real interview pressure. We even updated the interface so code runs on the right and conversation flows on the left, mirroring the exact online interview setup candidates experience at these companies.
System Design interviews
System Design interviewsdiffer even more across companies. Microsoft emphasizes methodical requirements gathering and structured diagrams. Meta pushes candidates to quickly address trade-offs at scale. Google interviews often progress in steps, with the interviewer steadily increasing complexity until you reach your limit. LinkedIn emphasizes real-world collaboration, while Netflix focuses on autonomy and decision-making under constraints.
On our platform, design tracks mirror these styles through diagramming tools and live follow-ups:
Microsoft-style design: Structured prompts requiring clear requirements, flow diagrams, and rational component choices.
Google/Meta design: Open-ended problems that evolve mid-session, with the agent interrupting to test your response to scaling and bottleneck challenges.
Amazon/LinkedIn/Netflix design: Scenario-driven sessions where you justify trade-offs in reliability, cost, or speed—mirroring the exact conversations you’d have onsite.
The difficulty ramps up dynamically, and the conversation style changes depending on the company’s culture. The diagramming tool plus live conversation makes it feel like sketching on a whiteboard with a real interviewer interrupting you at critical moments. It doesn’t mimic the structure of a design interview only. It also recreates the exact pressure and flow.
Behavioral interviews
Amazon is famous for assessing candidates based on Leadership Principles. Netflix focuses on independence and judgment under ambiguity. Meta and Google rely on collaboration, communication, and learning from mistakes. Oracle often mixes behavioral and technical questions to check the depth and range of knowledge.
Our behavioral tracks replicate this using natural conversations:
Amazon-style behavioral: STAR prompts quickly followed by pushback to test consistency and alignment with principles.
Netflix-style behavioral: Open-ended questions with high expectations for ownership and decision-making.
Meta/Google behavioral: Scenario-based discussions that emphasize teamwork, iteration, and clarity.
LinkedIn/Oracle behavioral: Focuses on adaptability, growth mindset, and technical leadership decisions.
These tracks replicate the feel of the interview: the interruptions, pacing, pressure, and scoring criteria. That’s what turns practice into real preparation. And candidates felt the difference immediately.
What do engineers say about AI-powered MAANG mock interviews?
Since launch, more than 15,000 interviews have been completed. Ratings have climbed from 2.5 in early beta to a steady 4.5. But the numbers matter less than what candidates themselves say.
One engineer told us, “This mimics the real interview.” Another wrote, “Much more effective than many of the interviews I’ve had with $200 coaches.” And one noted (also my favorite): “The bot does feel like a friendly interviewer. This is helpful.”
That’s the validation that matters most—not from dashboard metrics but from when candidates feel ready and walk away saying, ‘This feels real.’
The future of AI mock interview prep
Every meaningful feature, coding, diagram, voice, and detailed debrief came directly from user input. The next wave will, too.
We focus on making the voice even more natural, refining the coding environment to mirror real interview tools, and adding deeper answer analysis so candidates can track patterns across sessions.
The principle is the same as in the beginning: build realism, guided by the people using it.
The answer I was looking for
I’ve seen how interview prep often breaks down. As a candidate, I missed the feedback that really mattered. I watched strong engineers stumble as interviewers because their practice didn’t match real interview expectations.
That encouraged me to build a mock interview agent that feels closer to a real MAANG interview than anything else I’ve seen. It’s not perfect. But if practice feels like the actual game, you walk into the real interview sharper, calmer, and more confident.
For me, it began with one vague piece of feedback, the moment I realized I had no idea how I came across. Today, thousands of engineers walk away from mockinterviews.dev knows exactly how they performed, what they did well, and where to improve.
That shift, from uncertainty to clarity, is the answer I sought back then. And now, it’s available to anyone preparing for the interviews that matter most.
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