Modern Technical Interview Practices

𝗧𝗵𝗶𝗻𝗸 𝗬𝗼𝘂’𝗿𝗲 𝗥𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗡𝗩𝗜𝗗𝗜𝗔 𝗼𝗿 𝗤𝘂𝗮𝗹𝗰𝗼𝗺𝗺? 𝗧𝗿𝘆 𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝗧𝗵𝗲𝘀𝗲 𝗙𝗶𝗿𝘀𝘁. Everyone wants to get into NVIDIA, Qualcomm, Intel, AMD, Synopsys or Cadence. But very few prepare the way their interviewers expect. These aren’t just “difficult questions.” They represent the concepts companies repeatedly evaluate during technical interviews. Here are the lessons behind them. 1. Companies Test Fundamentals, Not Memorization. Most interview questions are built on a small number of core concepts: • Digital Logic • Verilog/SystemVerilog • FSM Design • Timing Analysis • RTL Design • Physical Design • Analog Electronics If your fundamentals are strong, even an unfamiliar question becomes manageable. 2. Every Question Has a Purpose. Notice how each company focuses on different skills. • NVIDIA → Counters, Sequential Logic • Qualcomm → Boolean Logic & Optimization • AMD → RTL Coding • Intel → Setup & Hold Timing • Synopsys → Static Timing Analysis • Broadcom → FSM Design • Texas Instruments → Combinational Logic • Samsung → Memory & FIFO • Cadence → Physical Design Flow • Infineon → Analog Circuits The question isn’t random. It’s checking whether you’re ready for the work you’ll actually do. 3. Interviewers Want Your Thought Process. Most candidates rush to the answer. Strong candidates explain: • Why they chose that approach • Alternative solutions • Trade-offs • Design considerations • Possible corner cases Thinking clearly matters more than answering quickly. 4. Theory Alone Doesn’t Get You Hired. After learning a topic: • Write RTL • Simulate it • Verify it • Find bugs • Optimize it • Document it Practical experience makes concepts stick. 5. Learn to Solve on Paper. Many technical interviews begin with: • Whiteboard discussions • Pen-and-paper design • Logic derivations • Timing calculations Practice without relying on an IDE. If you can’t explain your design, writing code won’t save you. 6. Companies Prefer Depth Over Breadth. Instead of studying 100 topics once… Master the topics that appear repeatedly: • FSM • Timing • RTL • Verilog • Digital Design • STA • Memories • CMOS • Physical Design Basics Depth creates confidence. 7. Every Wrong Answer Teaches Something. Don’t just solve questions. After every attempt ask yourself: • Why was I wrong? • Which concept was weak? • What assumption did I make? • How can I solve it faster next time? That’s how interview-ready engineers are built. 8. Interviews Don’t Measure Memory. They measure whether you can: • Analyze a problem • Break it into smaller parts • Communicate clearly • Think like an engineer The best candidates aren’t those who memorize the most. They’re the ones who understand the most. #VLSI #ECE #Semiconductor #ElectronicsEngineering #EmbeddedSystems #ChipDesign #GATEECE #VLSICompanies

Most candidates approach interviews like an exam—listing accomplishments and reciting rehearsed answers. But top performers? They showcase how they think, problem-solve, and make decisions in real time. Why does this matter? Because hiring managers aren’t just assessing what you’ve done in the past—they’re evaluating how you’ll perform in their company. Here’s how to demonstrate strong thinking and problem-solving skills in interviews. 👇 1️⃣ Think Out Loud: Let Them In on Your Thought Process Many candidates give only the final answer, but hiring managers want to see how you got there. ✅ Break problems into logical steps. ✅ State assumptions and clarify unknowns. ✅ Weigh trade-offs before reaching a conclusion. Example (for a problem-solving question): Interviewer: “How would you improve our customer onboarding process?” ❌ Weak response: “I’d optimize the emails and add a tutorial.” ✅ Strong response: "First, I’d analyze current user behaviour—where are the biggest drop-offs? If it’s lack of clarity, I’d improve messaging. If it’s complexity, I’d test simplifying steps. Balancing efficiency with engagement would be key to reducing churn while maintaining quality onboarding." 🔹 Why this works: It shows structured thinking, data-driven decision-making, and strategic problem-solving. 2️⃣ Use a Clear Answer Framework A structured answer is easier to follow and more impactful. ✅ For behavioural questions, use STAR or CAR: ✔ Situation/Challenge – Set up the context. ✔ Action – What steps did you take? ✔ Result – What was the measurable impact? Example: Interviewer: “Tell me about a time you improved efficiency on your team.” "Our team struggled with long approval times (Situation). I introduced an automated tracking system to flag delays (Action), cutting turnaround time by 40% (Result)." 🔹 Why this works: It’s concise, clear, and focused on impact. Aim for an answer that's about 2 min long. 3️⃣ Show Adaptability: There’s No “Perfect” Answer Many interview questions don’t have a single right answer—hiring managers want to see how you adapt your thinking. ✅ Acknowledge challenges or constraints. ✅ Offer multiple solutions with pros/cons. ✅ Be open to feedback and adjust. Example (for a strategy question): Interviewer: “How would you expand our product into a new market?” "There are a few ways to approach this. We could start with a pilot launch in a single region to test demand, or we could form strategic partnerships to gain traction faster. The right approach depends on factors like budget, timeline, and market research insights." 🔹 Why this works: It shows flexibility, strategic thinking, and an ability to weigh options. Interviews Are Not Just About Your Experience—They’re About How You Think. ✔ Think out loud—explain your reasoning. ✔ Structure your answers—keep them clear and concise. ✔ Demonstrate adaptability—consider different solutions. 👉 Found this helpful? Reshare to help others master interview thinking!

I almost rejected one of the best JavaScript developers I've ever hired. During his first technical interview, Adam (not his real name) struggled with a whiteboard coding exercise. His solution was inefficient, and he seemed to miss obvious optimisations. Based on our standard process, he was a clear rejection. But something caught my attention—his problem-solving approach was unique. While his code wasn't optimal, his questions revealed a depth of understanding about edge cases and potential pitfalls that most candidates missed entirely. I broke protocol and scheduled a second interview with a different format: a pair programming session on a real (sanitised) problem from our codebase. The transformation was remarkable. Away from the artificial whiteboard environment, Adam flourished. He wrote clean, elegant code, communicated his thought process clearly, and even identified a bug in our existing solution. Adam went on to become one of the top performers, leading several critical projects and mentoring junior developers. This experience fundamentally changed how I help to prepare hiring managers for interviews: __I now review candidate resumes looking for evidence of both technical skills AND problem-solving approaches __I suggest to prepare multiple assessment formats to accommodate different working styles __I always recommend to bring another technical interviewer for a second perspective Has anyone else had a similar experience with a candidate who performed differently across different interview formats? What did you learn? #TechHiring #InterviewExcellence #CandidateExperience

If you’re preparing for Software Engineering interviews in 2026… Stop practicing random questions. Interviews aren’t about luck. They follow patterns. This guide organizes the Top 40 Software Engineer Interview Questions into structured categories - from basics to advanced system design to behavioral rounds. 1. Basics (Q1–10) Big O notation Stack vs Queue Arrays vs Linked Lists Recursion vs Iteration OOP pillars Pass by value vs reference Hash tables Sync vs Async Binary Search Trees SQL vs NoSQL These questions test your fundamentals — algorithm efficiency, memory behavior, and database trade-offs. If you can’t clearly explain these, advanced topics won’t save you. 2. Intermediate (Q11–20) Reverse a linked list DFS vs BFS Dynamic Programming Detect cycle in linked list Process vs Thread Design LRU Cache Database indexes ACID properties REST vs GraphQL Design scalable architecture This is where companies evaluate problem-solving depth and practical system understanding. It’s no longer just coding — it’s design thinking. 3. Advanced (Q21–30) Design distributed caching (like Redis) CAP theorem Rate limiting Database sharding Microservices vs Monolith Eventual consistency Raft vs Paxos Real-time messaging systems Distributed database principles Fault tolerance & disaster recovery These questions test distributed systems thinking. You’re being evaluated as someone who can build systems that scale, not just solve coding puzzles. 4. Behavioral (Q31–40) Debugging production issues Handling difficult teammates Disagreements with managers Learning new tech quickly Architectural decision-making Estimating timelines Performance optimization Production outages Balancing tech debt Mentoring juniors This is where offers are won or lost. Technical skill gets you shortlisted. Behavioral clarity gets you hired. Most candidates prepare only for coding rounds. Top engineers prepare for: - Fundamentals - System design - Trade-offs - Real-world failures - Communication under pressure Interviews in 2026 aren’t just about writing code. They’re about proving you can think, design, scale, and lead. Prepare in layers - not randomly. That’s how you move from “candidate” to “offer letter.”

The other day, conducting a couple of technical interviews back-to-back perfectly captured where we are in tech right now. First candidate fired up Cursor, added the right documentation in its context, and started pair-programming with AI. They weren't just writing code; they were guiding the process — generating a PRD, scaffolding the implementation, and then applying their critical judgment to review and iterate on the output. Very much an orchestration role. The second candidate took a more traditional approach. Lots of manual typing, context switching to Google for syntax lookup, and digging through Stack Overflow for debugging errors. Honestly, it felt like watching two different eras of development unfold side-by-side. This experience reinforces why I'm leaning into an AI-first approach for interviews. No LeetCode. Instead, I want to see you at your best: • Bring the tools you actually use (yes, including AI). • Show me how you solve real problems • Demonstrate your thought process and engineering judgment. • Talk through the architecture and the trade-offs you're making. What I'm looking to see is their engineering judgment coupled with their productivity and efficiency. How effectively can they leverage the power of AI while maintaining the architect role and, ultimately, ship at an elite level? Observing someone write code without incorporating AI in 2025 increasingly feels like looking at a snapshot from a bygone era. What are your thoughts? How is this shift playing out in your hiring process or daily work? --- 👉 We're building our team with founding engineers who get this and are excited to define modern engineering practices from the ground up in the enterprise AI space. If that resonates, reach out!

In the last 10 years, I've applied to almost all the MAANG+ companies for Cloud & DevOps positions:  - Applied Twice at Google - Applied to Microsoft (Cleared 3 rounds) - Applied to Amazon Twice - Applied to Meta (for Production Engineer_ - Had a chance to sit in for the SRE interview at Apple Each time, I went through the hiring processes, I learned a lot from my experiences regarding industry standards & my skills. Here are my learnings from all the interviews (insights that are rarely talked about)  1. Confidence Opens Doors - Walk in with confidence, but back it up with examples from your work.   – Show them how you’ve done similar things before or learned fast on the job.  - Give specific examples of how you created solutions, the more detail you give, the more genuine you seem  2. Talk Out Loud: They Care About Your Thinking Process   - Coding rounds are less about the final answer and more about how you think.   - Always explain why you chose this algorithm, data structure, or approach.  - Example: If I ask you to sort a linked list and array, explain how you’d handle each input without hardcoding.   3. Problem-Solving >>> Memorization   - You won’t be asked standard questions all the time. –They want to see if you can break down problems into smaller steps.   – Focus on understanding the problem statement first.   - Example: At Google, questions often started vague, like “Optimize Spark performance.” You had to ask questions to clarify the scope before jumping in.   4. Business Impact > Fancy Code  - Interviewers love candidates who think about real-world impact, how their work improves systems, reduces costs, or handles failures.  - Don’t just explain your code. Say, “This approach scales better because…” or “This method reduces downtime during outages.”   5. Expect Tricky Questions & Learn to Adapt   - You’ll get questions that test your ability to learn on the go.   - They don’t expect you to know everything but want to see if you can stay calm, ask the right questions, and figure things out.  - Example: Amazon asked about migrating hot and cold storage. Even without prior experience, the key was breaking the problem into steps and proposing ideas.   6. Failures Are Normal, Show How You Recover  - Big Tech doesn’t expect perfect systems, they expect fail-safes.   - Prepare examples where something failed, and you recovered quickly.  - Example: They asked about a time when servers went down during peak hours. My answer focus was on how recovery systems reduced downtime instead of avoiding failures completely.   7. Simplify Your Approach. Don’t Overcomplicate   - Many candidates try to impress with complex answers and overengineered solutions. Don’t.   - Focus on clarity and efficiency. Explain why you’re choosing one approach over another.  - Example: For a database optimization question, start with indexing strategies before diving into custom caching layers.  Continued in the comments ↓

CS students: pay attention! How tech talent is scouted and how you will be interviewed is changing fast. Business Insider reports that companies like Cisco, LinkedIn, and Anthropic are rethinking how they find and hire software engineers. How they find you is changing. Startups like Replit and Cognition scout engineers by browsing X posts and GitHub pages. Replit's chief people officer calls X a "main medium" for recruiting. Employees flag talent in internal Slack channels. Your online presence is your first interview. How they evaluate you is changing. LeetCode alone won't cut it. Cisco replaced coding challenges with project-based exercises and AI-assisted environments to observe how candidates work, not what they produce. LinkedIn's head of talent acquisition says the top skills now are systems thinking, problem solving, and operating agentic systems. Anthropic runs a dedicated non-technical culture interview for every role. Miss on mission fit, you're out regardless of technical chops. AI startups like Cursor and Kilo run paid work trials where candidates ship real product with the team before getting an offer. Why? AI writes code now. Employers need people who understand what the code does, correct it when it breaks, and defend the decisions behind it. 3 things to practice now: 1. Explain your thinking out loud. Walk through a project: architecture choices, tradeoffs, what you'd change. This is the new interview. 2. Build with AI tools, then own the output. Use Cursor, Claude Code, or Copilot on a real project. Then explain every line, debug without the tool, and defend your approach. Direct AI, don't depend on it. 3. Sharpen your human skills. Communication, collaboration, culture fit. Companies are screening for these as hard as technical ability. Present your work. Get comfortable being questioned. The engineers getting hired think critically, communicate clearly, and adapt to tools that change every quarter. And they know how to promote their work and network. Follow me for more updates on early tech career trends and opportunities. Link to article in comments. #SoftwareEngineering #TechCareers #CSMajors #Internships #GenerativeAI #EarlyCareer #InterviewPrep

Today, I witnessed my team lead interviewing a Full-Stack Web Developer with 2 years of experience, and I observed some interesting insights. The interview started with basic technical questions, which the candidate answered well. Then, my team lead gave him a coding task: 👉 "Write a function that removes duplicate values from a nested array while preserving its structure (PHP)." The candidate struggled and couldn’t solve it. So, my lead gave him another task: 👉 "Write a function that groups an array of associative arrays based on a specific key." Again, he couldn’t solve it. At this point, he admitted: "I don’t always have syntax and logic rules memorized, as I usually rely on resources like Stack Overflow and ChatGPT. Could you assign me a real-world task instead?" Here,I decided to step in. Since I was working on WooCommerce project, I gave him this challenge: 💡 "Implement a functionality where a logged-in user gets a 15% discount if they place two orders in a single day." We allowed him to use Google, ChatGPT, or any other resources. To our surprise, within 1.5 hour, he understood WooCommerce functionality, analyzed the existing code, and successfully implemented the feature. ✨ We ended up hiring him! ✨ Key Takeaway Many traditional interview techniques overemphasize logical puzzles and syntax-heavy questions, yet modern developers approach problem-solving differently. Rather than memorizing solutions, they adapt, learn, and implement on the spot using the tools and resources available to them. 💡 Instead of testing how well candidates remember syntax, we should focus on how effectively they apply problem-solving skills to practical challenges. What’s your take on this? Let’s discuss in the comments! 👇 #HiringTechTalent #TechCommunity #WhatDoYouThink

If I had a system design interview tomorrow at Google, this is exactly how I’d approach it.   (This framework helped me crack 3 FAANG+ companies in the past, including Amazon.)  Most engineers fail system design interviews not because they lack knowledge but because they lack structure.  You don’t need to memorize 100 architectures. You need a structured and easy-to-apply approach that works every time.  Here’s how I break it down:   1/ Clarify the Problem Before Writing Anything   - System design interviews aren’t about throwing buzzwords—they’re about trade-offs.   - Start with scoping → Are we designing just one feature or the entire system?   - Ask constraints upfront → How many users? Read/write ratio? Latency requirements?   - Define success criteria → What matters most? Scalability? Cost? Low-latency?  Most candidates assume things and jump into solutions.   I make sure I know what we’re solving before I even start.   2/ Define Functional & Non-Functional Requirements Clearly   - Functional: What features does the system need?   - Non-functional: What are the performance expectations?   - What’s the biggest technical challenge? (This helps guide the discussion.)  Example: If we’re designing YouTube, is the focus on video uploads, recommendations, or live streaming?   Each has a different set of constraints.   3/ Estimate the Scale & Plan Capacity Like an Engineer   - Users per second? Requests per second?   - Storage needs? If we store 10MB per user and have 100M users, what does that mean?   - Throughput? Can a single database handle the load, or do we need sharding?  Most candidates throw random numbers.   I do quick, back-of-the-envelope calculations to validate my assumptions.   4/ Break the System into Core Components (High-Level Design)   - Define the major building blocks → API Gateway, Load Balancer, Service Layers, Databases.   - Don’t overcomplicate. Simple and scalable always wins.   - Clearly define the interactions between services.  If I’m designing a messaging app, I break it down into:   — User Service (auth, profiles)   — Messaging Service (storing chats)   — Notification Service (real-time updates)   — Media Storage (for images, videos)  Each has different constraints, so I build around what’s most important.  Continued Here: https://lnkd.in/eiHQs-qT P.S. Preparing for a software engineering interview? Check out my book, System Design and Behavioral Intelligence. It will help you: • Build strong System Design foundations and approach design problems with confidence • Develop practical frameworks to navigate complex behavioral and leadership challenges 📘 Paperback available on Amazon internationally: https://mybook.to/systemdesignbehavioralhttps://lnkd.in/eiHQs-qT P.S. Preparing for a software engineering interview? Check out my book, System Design and Behavioral Intelligence. It will help you: • Build strong System Design foundations and approach design problems with confidence • Develop practical frameworks to navigate complex behavioral and leadership challenges 📘 Paperback available on Amazon internationally: https://mybook.to/systemdesignbehavioral Check it out!

Most people walk into interviews ready to answer questions But top candidates know how to steer the conversation. Biggest mistakes? Generic answers, missing intent, and not showing problem-solving skills. Here are 9 tough interview questions (and how to avoid common pitfalls so you stand out): 1. “Tell me about a time you solved a problem with limited resources.” ➟ Mistake: Saying, “𝘐 𝘫𝘶𝘴𝘵 𝘸𝘰𝘳𝘬𝘦𝘥 𝘩𝘢𝘳𝘥𝘦𝘳” instead of showing creativity. ➟ Why: They want to see if you’re resourceful. ➟ Share a real example of adapting and delivering despite constraints. 2. “Give me an example of learning something new quickly.” ➟ Mistake: Saying, “𝘐 𝘭𝘰𝘷𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨” without proof. ➟ Why: They value fast learners. ➟ Talk about picking up a skill, tool, or process under pressure. 3. “Tell me about a time you disagreed with a team member. How did you handle it?” ➟ Mistake: Saying, “𝘞𝘦 𝘫𝘶𝘴𝘵 𝘢𝘨𝘳𝘦𝘦𝘥 𝘵𝘰 𝘥𝘪𝘴𝘢𝘨𝘳𝘦𝘦” without showing resolution. ➟ Why: They want to see professionalism in handling conflict. ➟ Show how you communicated, found common ground, and kept the team on track. 4. “What’s the biggest mistake you’ve made at work, and what did you learn?” ➟ Mistake: Saying, “𝘐 𝘤𝘢𝘯’𝘵 𝘵𝘩𝘪𝘯𝘬 𝘰𝘧 𝘰𝘯𝘦.” No one is perfect. ➟ Why: They want self-awareness and growth. ➟ Own the mistake, explain the lesson, and how it made you better. 5. “Tell me about a time when you had competing deadlines. How did you prioritize?” ➟ Mistake: Saying, “𝘐 𝘫𝘶𝘴𝘵 𝘸𝘰𝘳𝘬𝘦𝘥 𝘭𝘢𝘵𝘦” instead of showing strategy. ➟ Why: They want to see time management skills. ➟ Explain how you prioritized, delegated, and decided what to tackle first. 6. “Have you ever pushed back on a manager’s decision? How did you do it?” ➟ Mistake: Saying, “𝘐 𝘫𝘶𝘴𝘵 𝘧𝘰𝘭𝘭𝘰𝘸𝘦𝘥 𝘰𝘳𝘥𝘦𝘳𝘴.” They want critical thinkers. ➟ Why: They want professionals who challenge ideas respectfully. ➟ Show how you presented data, influenced change, and stayed professional. 7. “Describe a time you went above and beyond for a customer or teammate.” ➟ Mistake: Giving a vague answer with no impact. ➟ Why: They want to see work ethic and initiative. ➟ Share a clear example of going the extra mile and the results. 8. “If you started this job tomorrow, what would be your first 30-day plan?” ➟ Mistake: Saying, “𝘐’𝘥 𝘫𝘶𝘴𝘵 𝘭𝘦𝘢𝘳𝘯 𝘵𝘩𝘦 𝘳𝘰𝘱𝘦𝘴.” ➟ Why: They want to see initiative and role understanding. ➟ Outline a plan to learn, assess challenges, and add value quickly. 9. “What’s something about you that’s not on your resume but makes you a great fit?” ➟ Mistake: Repeating what’s already on your resume. ➟ Why: They want to see what makes you different. ➟ Highlight a unique strength, experience, or mindset with a compelling story. What’s the toughest interview question you’ve ever faced? --- ♻️ Share this post to help others preparing for interviews. 🔖 Follow Véronique Barrot for more. Want my 100+ best cheat sheets? Get them here: linkedandlift.com