Vipul Verma

Here’s exactly how I would crack a $150K+ SWE job at Microsoft in 6 months. (A real strategy my client used to land interviews at Microsoft, Meta, and Stripe) Too many engineers prepare hard to crack MAANG. But they lack a solid strategy. Here’s the exact roadmap I'll follow if I wanted to land a SWE job at Microsoft. Step 1: Resume + Role Clarity → Reverse-engineer the JD Study at least 10+ Microsoft SWE job listings. Highlight recurring keywords, must-haves, and preferred tools. → Rewrite your resume like a product pitch Show measurable impact: Good: “Improved load time by 43%” Bad: “Worked on performance optimization.” → Make it keyword-optimized for ATS without sounding robotic. Step 2: Master DSA the Microsoft Way → Focus on patterns, not problems Microsoft LOVES: Sliding window Trees (esp. DFS/BFS) Graphs Dynamic programming → Suggested Platforms: Leetcode (Microsoft tag), Neetcode(dot)io roadmap, Grokking series on Educative → 2 problems/day + 1 mock interview/week = compounding prep Step 3: System Design (yes, even for junior roles) → Start with High-Level Design (HLD): Learn how to design APIs, caching, rate limiting, and DB scaling. → Build a project where you actually implement what you learn. Step 4: Microsoft-Specific Behavioral Prep → Microsoft uses structured behavioral interviews Focus on “3As” framework: Action → Approach → Aftermath → Use real projects to show: Collaboration Adaptability Customer focus Engineering rigor → Prep 8–10 STAR stories mapped to their values Step 5: Mock + Real-World Practice → Do 4–5 peer or mentor-led mock interviews (especially for behavioral + design) → Record yourself. Watch for filler words, unstructured answers, lack of metrics. → Apply for 5 roles/week—Microsoft + similar-sized companies (to get into interview flow) Step 6: Apply Strategically + Use Referrals → Connect with 3–5 engineers/recruiters/week on LinkedIn. Don’t ask for a referral right away - engage first. → Reach out using tailored messages like: “Hi [Name], I’ve been preparing for an SWE role at Microsoft. Your journey from [X] to [Microsoft] really stood out. If you're open, I’d love to learn more about your experience.” → Submit applications using referrals whenever possible + 1-click apply where relevant The client who followed this playbook now works at Microsoft Azure and had 3 competing offers before accepting. It’s not about doing everything. It’s about doing the right things in the right order. Give yourself 6 months and follow this roadmap to make it a reality. Share this with someone who dreams of working at Microsoft. P.S. Follow me if you are a job seeker in the U.S. I share practical advice that gets you land $150k+ roles in top companies.

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You are in a system design interview at Amazon for an SDE-3 role and the interviewer has given you the question to Design Netflix. He then asks a follow up to you: How does Netflix knows when to show: Are you still watching? Is it just time-based? If it were time-based, you would see it every time. So what is actually going on under the hood? Both look like a simple pop-up. Underneath, it is a mix of product thinking, client logic, and backend events. Btw, if you’re preparing for system design/coding interviews, check out our mock interview tool. You can use it for free here: https://lnkd.in/gpCn7t2T [1] Start from the product goals The feature is not only about nagging people. - Save bandwidth and CDN cost if the viewer has slept or walked away - Avoid autoplaying potentially sensitive content in an empty room - Protect kids if parents start a show and leave the TV on - Do all this without annoying active binge watchers So the design must answer one question: "When is the user probably not here any more" [2] Naive design - pure timer Simplest idea: - If playback has been running for 2 hours, show Are you still watching - If user clicks "Yes", reset the timer Why this is weak: - Someone can binge 5 episodes in a row and gets interrupted in the middle of an intense scene - Someone can start a show, walk away after 5 minutes, and the platform will keep streaming for the next 2 hours - It ignores how many episodes were auto played, device type, time of day, user habits  [3] Realistic design - session and engagement signals Think in terms of a "watch session" and "engagement events". Signals the client can track: - Play, pause, seek, volume change - Episode finished and next episode auto started - Remote or keyboard input, UI navigation - Screen on or off events from the device - For mobiles: app background or locked state A common heuristic could be: - Count how many episodes have auto played without any user interaction - Track how long it has been since the last button press or navigation - Only trigger the prompt when both are high enough Example rule: - If 3 episodes in a row have auto played - And there has been no interaction for 45 minutes - Then, before starting episode 4, show Are you still watching This feels much smarter: - Active viewers who pause, skip intro, change volume keep resetting the "engagement clock" - Sleeping viewers do not touch anything, so the next episode is blocked by the prompt  [4] Client heavy vs server heavy design You can talk about two design choices. Client first: - All logic runs inside the app on TV, mobile, or web - Client keeps an in memory session model and decides when to show the popup - It still sends telemetry events to backend for analytics and future tuning Pros: - Works even with flaky network - Highly responsive, no extra server round trip Cons: - Logic must be implemented and updated across many platforms - Harder to quickly roll out rule changes Continued ↓

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Just heard Amazon Prime laid off so many employees, Because they shipped faster and automated things with AI. And this just scares me but at the same time I feel there’s huge gap between how managerial side is thinking about AI v/s the Reality of AI: Because: 1. AI writes the code but fails at quality, I need to spend time to get good final version 2. Doing LLDs with AI is too hectic, it skips what it doesn’t understand or comes up with solution which isn’t optimised 3. HLDs aren’t even AIs cup of tea yet This just about coding part, there’s lot of more that we do as engineers. So, is it managers illusion which is taking away job ? But whatever it is someone needs to wake these companies to reality and show how important engineers are for the company. Done with listening AI can replace engineers.

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$100,000 for an H-1B visa. What does this mean for Indians? Every Engineer dreams of studying in the US and secure a job in the Silicon Valley. But Trump’s new H-1B fee changes that. Students may be forced to return earlier than planned because companies will now think twice before sponsoring visas. But you don’t need to go to US to earn in dollars. With the right skills and network you can earn in dollars while living in India. And this H-1B visa fee might change the hiring process in MNC’s too. Remote jobs will now get a spotlight. So here are some of the best portals where Indian engineers can land US remote jobs (links to all portals are in comments) ✅ Flexiple: Curated freelance network for top engineers and designers. ✅ Turing (dot) com: AI-powered platform that matches Indian developers with US companies. ✅ We Work Remotely: One of the oldest and most trusted boards for global remote roles. ✅ AngelList Talent (Wellfound): Startup-first hiring platform, many remote roles listed by US founders. ✅ RemoteOK: Job board focused on tech and product roles across geographies. ✅ Contra: Great for independent creators, developers, and freelancers targeting US startups. In 2025, opportunity isn’t tied to one country. It follows skills that prove their worth anywhere.

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I do not really post a lot about tech stuff, but maybe I can start. Here is a paper I read recently. ROSE: Robust caches for Amazon product search [https://lnkd.in/gPhbgHHQ] Thanks to Arpit Bhayani's paper shelf recommendations. The insights from this are really fascinating for the search world and how Amazon always knows how to find what you are looking for even if you have clumsy fingers like mine. For example if you are looking for shoes, you might type something like: “nike shoe,” “nike shoes,” “nikes shooes.” A normal cache treats these as different, so it gets big and slow, and you miss the cache a lot. Amazon’s ROSE fixes this by caching the intent, not the exact spelling. What ROSE essentially is, is a typo/variant-tolerant cache for product search that groups similar queries into the same “bucket,” so most lookups hit fast without growing memory. It’s deployed in Amazon Search and improved both latency (single-digit ms for most traffic) and business metrics. How this works is even more fascinating. 1. Locality-Sensitive Hashing: Similar queries collide in the same bucket. • Lexical-preserving hashing (character n-grams + minhash) for typos/variants. • Product-type-preserving hashing (weighted minhash) to keep the category intent (e.g., “dishwasher” vs. “dishwasher parts”). 2. Reservoir sampling: caps each bucket so memory stays constant even as queries grow. (Super convenient, and one of the many engineering gotcha's people might miss) 3. Count-based k-selection: avoids pairwise similarity inside buckets; just count collisions across hash tables → near constant-time retrieval. Where we can apply some of these concepts with Gen-AI (which is in many flavors essentially a search problem with extra steps)- Semantic prompt cache: Group paraphrased prompts to reuse an answer/logits/KV cache even when text isn’t identical. Can be helpful in saving your tokens and $. RAG request cache: map similar user questions to one retrieval plan to cut vector searches + reranking cost. [I remember Azure AI search having a RAG cache, but I am not sure how that worked] Tool/agent call cache: Dedupe near-duplicate function/tool invocations (e.g., same API call phrased differently). Eval & feedback loops: Bucket similar generations or errors to reuse critiques/patches. Support/search front-ends to LLMs: Typo-tolerant, intent-stable pre-rewrites before hitting the model, ensure we only use tokens when absolutely needed.

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Silicon Valley was built on Indian talent 🇮🇳, and now it’s our turn to create that ecosystem here. Talent alone isn’t enough — India needs stronger R&D funding, better AI infrastructure, and the courage to innovate beyond just copying models. If we get the intent right, “Silicon Valley Bharat” won’t just be a dream — it can be our reality. 🚀

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SDE-2s ask, How do I build this? Staff Engineers ask, Should we build this at all? To reach the next level, you must stop being a task receiver and start being a problem refiner. Senior engineers focus on execution, Staff and Principal engineers focus on the business case. If you can save the company six months of engineering effort by proving a feature isn't necessary that is a bigger win than writing the most elegant code in the world. Start questioning the why before you optimize the how. How do you navigate work? Comment below 👇 #softwareengineer #career #growth

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Two major news from US this week that Indians should care about: 1/ Amazon’s 14000 layoffs (which is not because AI is efficient but because its costly) 2/ Walmart putting a pause on all H1B sponsored hiring While the $100K H1B fee update exempted all students in US from paying $100K for the H1B lottery, things are still turbulent. Layoffs are not cooling off and companies are still wary of sponsoring H1B visas. This is why many people who went to US in last 2-3 years (before policies worsened) are feeling stuck there. They took huge loans with a bet that is not playing out the way they hoped. Does it mean their US education is going to be a waste? Not really. You can still leverage it to build a global career but you need to be smart about how and where to use it. That is what I discuss in today’s YouTube video - 7 concrete strategies for Indians feeling stuck in the US! What they can do to safeguard their interests? (YouTube link for full video mentioned in the first comment) PS. Join 75,000 people who watch our weekly long analysis and tips on the YT channel (Scholar Strategy TV) if you haven't 🙏 #scholarstrategy #24northstar

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