Venkateswaran Venkatasubramanian

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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There is a quiet habit in many organizations that I believe we should rethink. Calling engineers "resources". At first glance, it may sound harmless. But words shape culture, and the difference between an engineer and a resource is deeper than terminology. A resource is something you allocate. A resource is interchangeable. A resource is planned in spreadsheets and capacity charts. But an engineer is something entirely different. An engineer is a problem solver. An engineer designs systems that didn’t exist before. An engineer takes ambiguity, constraints, and trade-offs and turns them into working reality. You cannot allocate creativity. You cannot schedule curiosity. You cannot spreadsheet innovation. When we call people "resources", we unintentionally reduce them to units of output. When we call them engineers, we acknowledge their judgment, craft, and ownership. The difference shows up in how teams behave. When people are treated like resources: • Work is assigned. • Thinking is minimised. • Ownership is diluted. When people are treated like engineers: • Problems are shared. • Ideas are encouraged. • Systems improve. Great companies are not built by managing resources efficiently. They are built by empowering engineers to think, question, and create. Language matters because culture follows language. Maybe the shift is simple: Instead of asking, "Do we have enough resources?" Ask, "Do we have the right engineers solving the right problems?" Small change in words. Massive change in mindset. #eBay #EngineeringCulture #Leadership #TechLeadership #SoftwareEngineering

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The PRD (product requirements document) is not dead. It may be back, and it may need to be better than ever.

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Positive thoughts to my ex-colleagues at Amazon. A tough period indeed. The first few days after a layoff feel surreal. The silence after the news can be deafening, and the urge to immediately fix everything is strong. But here’s the truth most people don’t tell you , it’s okay to pause. Rest. Let your brain and body catch up to what just happened. Clarity only comes after you exhale. Once you’ve had time to breathe, start treating the next chapter like a strategic reboot. Use this moment to re-evaluate what kind of work excites you and what kind you never want to return to. The layoff might have closed a door, but it also cleared space for something better aligned with who you are now. When you’re ready to re-engage, AI tools can take a lot of the heavy lifting out of the job search. ChatGPT can help you tailor your résumé for each role and practice interview questions. Tools like Rezi and Teal can optimize your resume for applicant tracking systems. Even LinkedIn’s own AI features can help you write stronger “About” sections or suggest better keywords to match your profile with recruiters’ searches. Use this time not just to find another job, but to redesign your career story. A layoff isn’t the end of your professional identity. It’s a forced pause that can turn into your most creative rewrite yet. #Layoffs #CareerGrowth #AItools #JobSearch #Resilience #LinkedInCommunity

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Why the Amazon Layoffs are a Wake-up Call for Every Engineer The era of "pattern-match engineering" is closing. Recent reports show Amazon cutting thousands of corporate roles as part of a deeper pivot toward AI-augmented productivity. This isn't just about cost-cutting; it's a fundamental shift in what the industry values. Any work that relies on applying matured designs to new cases by following a set pattern is now within the territory of AI. If your value is purely in the execution of these patterns, that value is rapidly depreciating. To remain non-replaceable, we must shift our focus: From Execution to Architecture: Don't just follow the pattern; design the system that leverages it. From Bandwidth to Innovation: Use AI to handle the repetitive "heavy lifting" so you can reclaim your time for new, high-value innovation. From Coder to System Shaper: The most valuable engineers today aren't just writing code; they are directing AI to produce results and solving the complex problems AI can't yet touch. The goal isn't to compete with AI—it's to leverage it to expand your own impact. In an industry that is "right-sizing" through technology, those who use AI as a force multiplier will be the ones who lead the next wave. https://lnkd.in/g2RHA4zr

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As an SDM at Amazon, I often hear concerns that “𝗜𝗻𝘀𝗶𝘀𝘁 𝗼𝗻 𝘁𝗵𝗲 𝗛𝗶𝗴𝗵𝗲𝘀𝘁 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀” can become a bottleneck to delivery. That hasn’t been my experience. When practiced well, this principle isn’t about adding friction at the end of the process or holding teams to vague perfection. It’s about investing in a learning culture where engineers understand 𝘄𝗵𝘆 the bar exists and are empowered to raise it themselves through better design reviews, stronger testing, and clearer ownership. High standards, when paired with strong coaching and trust, actually accelerate delivery. Teams spend less time reworking issues, firefighting in production, or debating expectations. Instead, they build confidence in what they ship and continuously improve how they build it. For me, insisting on high standards is less about saying “no” and more about helping teams learn faster, make better trade-offs, and sustainably deliver great software at scale. That’s not a bottleneck but a leverage. The real question isn’t whether to insist on high standards, but how to practice them in a way that enables teams rather than slows them down. I’ll dive into that in my next post.

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We once had a backend incident that, on paper, should've been a major outage. But nothing happened. Users kept browsing, ordering, and moving on with their day. Our fallbacks quietly took over. Under our old metrics, this would've counted as downtime. But was it really? That question changed how we think about uptime at Meesho. We stopped measuring system health and started measuring user outcomes - can the customer complete their order? Can the seller fulfill it? Simple shift. Massive impact on how we engineer, invest, and own reliability. https://lnkd.in/gqDnb9q3 Siddharth Gupta Amlan Sekhar Das

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FINALLY!! In addition to the push from government, wall street, and customers, there is now a push from employees too!! In a bold move, over 1,000 Amazon (India) employees are raising concerns about the company's rapid AI expansion. They're calling for sustainable practices and ethical guidelines to protect jobs and the environment. (You can read more about their demands here: https://lnkd.in/gxrnRZKs) It's crucial for tech giants to balance innovation with responsibility. As we embrace AI, let's ensure it's a force for good, not a source of harm. What are your thoughts on this?

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6 months into my job at Amazon, I broke production and thought this was the day my career ended because the company lost a lot of revenue...This is the story of one of the biggest learnings of my career. I was on an ad tech team. I was trying to learn the ad pipeline, went a bit too confident with a change, and triggered a failure. For 7–8 hours, ads were not being served. The moment I realised what had happened, my heart just dropped. I pinged my senior, called it out clearly, and stayed glued to the incident channel. Here is what surprised me. No one shouted. The first questions were: - “What changed?” - “How do we restore?” - “How do we stop this from spreading?” We rolled back. Checked the data. Verified that the pipeline was healthy again. Only after things were stable did the real work begin. That was the retrospective. We walked through the timeline, and I explained exactly what I did. We realised there was a big documentation gap. Some critical behavior of the pipeline lived only in people’s heads, not in any design doc or runbook. The conversation was not “Why did you mess this up.” It was “How did the system allow this to happen and how do we make sure the next person does not fall into the same trap?” I wrote the COE (Correction of Error) doc. My senior manager liked how honest and detailed it was. He told me, “You made a serious mistake. You also did the right things after. You reported it quickly, stayed on the fix, and left the system stronger than you found it.” That has stayed with me ever since. A healthy engineering culture does not pretend failures will never happen. It assumes they will. So it rewards people who: - Surface problems early instead of hiding them - Take ownership of their changes - Care enough to improve docs and processes after an incident - Focus on learning, not on finding a scapegoat Looking back, that outage was expensive for the company, but priceless for me. It taught me that good teams judge you not only by the bugs you ship, but by how you behave when those bugs show up in production. You will break something important at some point in your career. The question is not “How do I avoid mistakes forever?” It is “When it happens, will I have the courage to own it and the maturity to learn from it?”

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Amid new H-1B visa curbs getting implemented this year, the major US tech firms-Facebook (Meta), Amazon, Apple, Microsoft, Netflix, and Google (Alphabet) closed 2025, collectively hiring over 32,000 new employees in India. FAAMNG companies reported an 18% YoY uptick in hiring in specialised roles in India, with another 3,000-5,000 active job openings. https://lnkd.in/gynKeext

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