How to Prepare for Your IT Job Interview with Mock Interviews

When was the last time you heard candidates praising a technical interview process? A $500B+ publicly-traded company just adopted our next-gen hiring process, and candidates love the experience. Our next-gen hiring process includes evaluating • Fundamentals of software engineering without an AI assistant • A real-world task on a code repo with an AI assistant in a Cursor-like IDE experience • Reviewing code written by an AI agent When you interview developers in contexts that mirror real work, you get stronger signals and a better candidate experience.

🤖 ML Engineers: The First Line That Predicts Interview Probability Your intro decides your fate. Before they scroll, before they read code – they scan the first line. And that line decides if they keep reading or close the tab. Most ML Engineers start with what they built. Wrong move. The best ones start with why it mattered. Recruiters aren’t parsing TensorFlow stacks. They’re scanning for business translation. Impact. Measurable results. A reason to trust you in production. 🔥 Here’s what a weak line looks like: “Built a customer segmentation model using K-Means.” That’s what every portfolio says. 🔥 Here’s what a top 1% line looks like: “Segmented 1.2M customers – cut churn by 18% in 4 weeks.” See the difference? One shows activity. The other shows outcome. 💡 That first line tells recruiters one thing: You don’t just code models – you solve revenue problems. Stop burying your value in technical details. Lead every project with impact first, and let the tech follow. Your model’s F1 score doesn’t sell you – your context does. One clear business result per project turns your portfolio into a credibility engine. 🔥 Start rewriting your top line today. 🔥 Follow JobLanderz.VIP for hidden ML job hacks that boost callbacks. 🔥 Save this post – use it when rebuilding your GitHub or Notion portfolio. #MachineLearning #AIJobs #MLEngineer #PortfolioDesign #CareerTips What’s the first line of your top project right now – impact or activity?

𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗴𝗿𝗶𝗻𝗱 𝟭𝟬𝟬𝟬+ 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. You just need to understand why you’re solving them. 😯 Most interviews don’t test how fast you code — they test how well you think under pressure, how clearly you explain your approach, and how you deal with the unknown. Here’s how to prepare smartly, not blindly: 1. Pick 10–12 core DSA patterns (Sliding Window, Two Pointers, Binary Search, Graphs, DP, etc.) 2. Solve 4–5 problems for each pattern 3. After every question: Note down the pattern Explain your solution out loud Track what confused you Mock interviews > Doing more questions Some of the most frequently asked LeetCode topics in interviews: 🔹 Arrays: Two Sum, Merge Intervals, Kadane’s Algorithm 🔹 Strings: Anagrams, Longest Substring, Palindromes 🔹 Trees: Inorder Traversal, Lowest Common Ancestor 🔹 Graphs: BFS/DFS, Cycle Detection, Dijkstra 🔹 Dynamic Programming: House Robber, LIS, Knapsack Focus on depth over volume. Even 250–300 well-practiced questions are enough if you truly understand the patterns. In an actual interview, it’s not just about solving the problem — it’s about how you communicate, how you think, and how you navigate stuck moments. Be calm. Be clear. Be consistent. 🚀 Planning to work remotely in 2025? list of 700+ companies hiring for remote roles with great packages! 👉 Check it out: https://lnkd.in/gkcRfEXk 📄 Need an ATS-friendly resume to get shortlisted? Grab this free, high-converting resume template 👉 https://lnkd.in/gx64i6vE 🚀 2025 is the year of SKILLS, not degrees! 💡 The right course can 10x your career, salary, and opportunities. 🔥 These industry-recognized programs are trusted by top companies worldwide. 🎯 Career-Changing Courses You Must Take in 2025! 🔃 7000+ free courses free access: https://lnkd.in/gedz7JjN ✅ Google AI Essentials 👉 https://lnkd.in/guyNfZ_y ✅ Google Data Analytics Professional Certificate 👉https://lnkd.in/gBfiP64c ✅ Google Introduction to Generative AI 👉 https://lnkd.in/gN2iNvEX ✅ Machine Learning Specialization 👉https://lnkd.in/gTYUNj38 ✅ Google Cybersecurity Professional Certificate 👉https://lnkd.in/g8xSS65z ✅ IBM Data Analyst 👉https://lnkd.in/gedz7JjN ✅ IBM Data Science 👉https://lnkd.in/gRKyhjSp ✅ Microsoft Power BI Data Analyst 👉https://lnkd.in/gMiWy9u4 ✅ Introduction to Generative AI 👉https://lnkd.in/gN2iNvEX ✅ Python Basics for Data Analysis 👉https://lnkd.in/gwSnE8a8 ✅ Web Development with HTML, CSS, JavaScript 👉https://lnkd.in/ghefjWSf ✅ Foundations of Project Management 👉 https://lnkd.in/gT5KrCpz ✅ The Structured Query Language (SQL) 👉https://lnkd.in/gsWZcaUg ✅ Data Analysis with R Programming 👉 https://lnkd.in/gR3g42T8

Just leaving this here for any future candidates who will have the luck (or misfortune) to do job interviews with me: PLEASE DO NOT USE compilers or interpreters of any kind before, during or after our interview! I promise you, with 98% certainty, that I will know, and you will be immediately disqualified upon me realizing it. Compilers and interpreters are dumbing you down and deskilling you long term, and this is something I've kept saying for months now. The evidence is out there and data in support of it is mounting. DYOR! If you want to be a capable Software Engineer, you must be able - at all times - to write machine code by hand on your chosen platform and also reason through any problem, fully on your own and without an assembler. Not to clown on Alex Ragalie too much, I'm sure it's coming from a place of legitimate concern, but this is what you sound like when you ignore new, powerful tools. When I was younger, I, too turned my nose up at people who couldn't, say, handle memory management in C++ without smart pointers - "they're an inefficient crutch," I'd say, and probably rejected candidates not because there was anything actually wrong with them, but because I needed to prove something about *myself*. For people I interview, I want you to know how to maximize these tools as well as when to take over yourself, and it's up to me to figure out how to fairly test you so that I can see both. Don't stay stuck in the past, but don't outsource your brain, either.

I'm not really a huge fan of LeetCode style interviews. But, most of the big tech companies operate this way so I don't have much of a choice when interviewing candidates. I notice awhile ago my coding problems became seemingly less challenging all of a sudden. So, I plugged the question into ChatGPT and it sure enough it nailed it! Perfect answer. Remember to always talk through the problems in your interviews. It's more about your thought process then the actually problem. In this new AI era, problem solving skills and asking the right questions are more valuable then ever. #AI #interviewing #coding

How do some Analysts land 25–50 LPA interviews… with just SQL and Excel? While others keep applying for months and get ghosted? Here’s what those smart ones do differently 👇 1️⃣ 𝗧𝗵𝗲𝘆 𝘁𝗮𝗿𝗴𝗲𝘁, 𝗻𝗼𝘁 𝘀𝗽𝗿𝗮𝘆. They pick 20–30 dream companies.  Then go deep instead of wide.  Quality over desperation. 2️⃣ 𝗧𝗵𝗲𝘆 𝗿𝗲𝘃𝗲𝗿𝘀𝗲-𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗝𝗗𝘀. They don’t copy ChatGPT resumes.  They match their projects to the top skills in JD. 3️⃣ 𝗧𝗵𝗲𝘆 𝗼𝘄𝗻 𝘁𝗵𝗲𝗶𝗿 𝘀𝘁𝗼𝗿𝘆. They prepare answers about career gaps.  The smart ones frame every move as growth. 4️⃣ 𝗧𝗵𝗲𝘆 𝗯𝘂𝗶𝗹𝗱 𝗿𝗲𝗮𝗹 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀. Not fake “Hey bro, referral please?” DMs.  They network months before they apply. 5️⃣ 𝗧𝗵𝗲𝘆 𝗳𝗼𝗹𝗹𝗼𝘄 𝘂𝗽 𝗹𝗶𝗸𝗲 𝗴𝗵𝗼𝘀𝘁𝘀. No drama. Just calm, consistent nudges.  The kind that get remembered. Do this right and interviews start coming to you, not the other way around. 👇 Which of these are you already doing? ——— I'm Inshpreet Kaur, follow me for more Daily Tips ♻️ Repost this if you found it insightful! #analyst #analytics

Hiring is a data matching problem. But for too long, we've been using the wrong data: a one-page resume. At Simera, we knew we had to go deeper. We built our AI, 'Agent Era,' to solve this. Instead of just matching keywords, Agent Era analyzes over 5,000 data points on each candidate, from skills assessments and smart interviews to work history. It's not about just finding a 'Full Stack Developer.' It's about finding the right one for your team, culture, and goals. It's how we deliver a shortlist in minutes, not weeks

I was once headhunted by a recruiter for a software role. Normally, I’d decline, either due to a mismatch in the role or the dreaded “5 days WFO” line. But this time was different. ✔️ Remote-first role ✔️ Great pay ✔️ A fascinating role at the intersection of frontend and AI So, I decided to give it a shot. I hadn’t done structured interview prep in years, so I knew it would be a challenge. And as it turned out, the process was a firsthand glimpse into how interviews today have evolved, or rather, devolved, into a culture of question banks. My quick prep strategy was simple: - Check LeetCode, Glassdoor, Medium, and Blind for any shared interview experiences for that company. - Prepare those exact questions: I suspected it would be a question bank-style process. - Review my projects, work history, and personal initiatives. Round 1 A live LLD coding round focused on frontend implementation. As expected, the question I received was identical to one shared online. That realization was bittersweet. Without prior rehearsal, solving that within 60 minutes would have been near impossible. It almost felt like they expected candidates to find the questions, rehearse them, and then deliver polished solutions. I managed to complete most of it because I’d seen it before. Had I relied solely on intuition and problem-solving experience, it would’ve easily taken 2 hours. Round 2 Mixed DSA and JavaScript, 3 questions total. Again, all were familiar from online discussions. This time, though, I hadn’t rehearsed. I solved 2 out of 3 before time ran out, losing precious minutes debugging edge cases. Result? Rejected. None of those questions tested originality, judgment, or even creativity, things AI can’t mimic meaningfully yet. Still, I’d say it was a good experience. The rounds focused more on practical coding than obscure DSA puzzles, which is at least a small step forward. As long as timed, question-bank-style interviews dominate, this is the game candidates are forced to play. But it raises deeper questions: what kind of talent diversity are we nurturing? Are we selecting people who build, think, and design, or those who memorize, rehearse, and regurgitate? And when people use AI tools to “cheat” through these rounds, it becomes a genuine risk for companies too. Bottom line 1. If you know a company reuses specific questions, prepare them. There’s no way around it right now unless your muscle memory is exceptional. 2. Rejections happen. Don’t take them personally. Most are due to interview mismatch, not skill mismatch, especially if you already have a strong track record. 3. Interviews feel tougher today because more candidates are grinding question banks full-time. For those balancing jobs and life, it’s not an equal playing field. These rounds rarely test originality; they test preparation. A strange reality. For now, it’s the game we all have to play. If you don't want to play it, check my previous post :P