Aman Raj

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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When you join a new org or switch teams in the same, it is quite an unsettling and borderline anxious experience. Here are a few things that I did to ramp up faster. 1. I consciously made an effort to remain unblocked 2. I read a ton of code and its history, sometimes even unrelated 3. I asked questions about the past, the present, and the future 4. I took up relatively mundane tasks - cross-team work, tests, and docs 5. I extended a helping hand in whatever capacity I could 6. I proactively met skip-level leaders and managers to understand the vision Some companies do have a culture of pairing up with someone existing in the team, but even if you do not get a mentor, it is important that you still navigate the situation and ramp up as quickly as you can. The above list is not exhaustive, by any means, and hence you can always add things that you find helpful in your context. but the lowest common denominator is to show extreme intent and interest to get output and drive outcomes. Hope this helps.

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Amazon Layoffs Incoming — And This Time, It's Principal Engineers Rumors[1] are circulating that Amazon is preparing another round of layoffs — and what's especially striking is that principal engineers are among those being targeted. This is a sharp reminder that: - Being senior doesn't make you immune to layoffs - As you climb the eng ladder (which is just another term for the corp ladder), you have to sustain your performance/impact, so cruising is not an option compared to common beliefs - Companies are entities that try to maximize revenue/profits – being laid off sucks, especially if you're on parental leave (or any kind of leave), on a precarious visa situation etc. I think we have seen enough layoffs in tech companies that these are probably the new normal. Companies who struggle to fire people through a regular process, will rely on layoffs to occasionally clean house. This sucks.

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The best advice I got on my promotions as a software engineer was from a Principal Architect at Amazon after he told me his story of getting promoted to Principal, and this has stayed with me my whole career. He said, very simply: > Your architecture can be a love letter to your technical brilliance, > but if it does not move customer and company goals, it will not move your level. That hurt a little. Because I recognised myself in it. A few years ago I wanted to design something with every shiny thing in the book: API Gateway, Lambda, DynamoDB, event bus, the works. On paper it was beautiful. In reality it was expensive, slow to ship, and did not match the skills of the team that would maintain it. So we shipped a hybrid architecture instead. Fewer moving parts, more reuse of existing services, cheaper to run, easier to hand over. Over time, here is how my thinking about promotions changed. 1. Start from the business goal, not the tech stack Before talking about Kafka vs SQS or MySQL vs DynamoDB, answer this first: What money, risk, or customer pain will this remove? Promotions track that, not the number of services in your diagram. 2. Ship finished products, not clever demos Leaders remember the feature that went live, stayed stable, and earned or saved real money. Drive the entire lifecycle yourself: design, build, launch, oncall, cleanup, docs. 3. Make the team faster, not just yourself Tools, scripts, templates, better CI, better dashboards. If people quietly say “things move faster when you are on this” your manager already has half a promotion case. 4. Do your share of the unsexy work Oncall, migrations, bugs, incident reviews. Senior folks who avoid this slow the entire org. Senior folks who lean into it become trusted very quickly. 5. Grow people, not just systems Mentor juniors, run design reviews, write clear docs, set standards. When your manager sees that you are already acting like the next level, formal promotion becomes a catch up, not a favour. 6. Be proactive about your promotion, not obsessed with it Have periodic one to ones. Talk about where you want to go and ask what evidence your manager would need to support that. Remember your manager has ten other people to manage. It is your responsibility to make your impact visible. Do not be desperate. Understand the timelines and work within them. As you get close to the boundary, collect the missing data points: impact metrics, incident write ups, design docs, peer feedback. Make the story easy to tell. Most importantly, work for the business, not for promotion-driven development. If your roadmap only exists to tick boxes on a promotion rubric, people can feel it. If your roadmap clearly creates value, saves cost, or reduces risk, promotion becomes the natural side effect. So instead of asking “What new tech can I use this year?” Ask, “Where can I create obvious business impact and leave the system and the people around me better than I found them?”

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One thing your manager will never tell you Your promotion depends less on your performance and more on whether your team is actually growing. When I joined Amazon as an SDE-1, my team had 25 people. By the time I became Senior, it had 120+. Looking back, my “fast promotion” wasn’t magic. The org was expanding like crazy and growth creates opportunity. Here is the part people don’t like hearing: ❌ If your team hasn’t grown in years, your career won’t either. ❌ It doesn’t matter how good you are. There is nowhere to go. ❌ The only real path up becomes waiting for someone above you to leave. So if you want to grow faster: Choose teams that are growing. Not teams that are comfortable. Skills matter. Environment matters more.

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For the engineer who feels busy but not productive: This 5-minute morning habit I use at Amazon will change your week. Most engineers plan only for the sprint, which keeps them reactive. The ones who grow the fastest think in multiple timeframes: day, week, quarter, and year. Here is how I structured it: ↳ Day: Plan your "Big 3" before checking Slack or email. ↳ Week: Every Monday, review your quarterly goals and prioritize 2–3 workstreams. ↳ Quarter: Do a role gap analysis, define what to focus on. ↳ Year: Define a theme that matches your life stage. This system creates feedback loops that connect your code today to your five-year vision. It keeps you from drifting into busywork and helps you grow with intention. 👉 Join 20,000+ engineers reading the full article here: https://lnkd.in/dQhnPzJB ♻️ REPOST to help someone today #softwareengineering #coding #programming

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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 tech job seeker in the U.S. I share practical advice that gets you hired. — Additional resources: https://lnkd.in/eKxQmYtP. https://lnkd.in/gwRWpXR9. https://lnkd.in/g94_Cziv

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Stop treating Load Balancers and Reverse Proxies as the same thing. In System Design interviews (and production incidents), the distinction matters more than you think. As we move from SDE1 to SDE2, we stop asking "How do I make this code work?" and start asking "How does this system scale and survive failure?" Here is the deep dive on two critical components that often get conflated. 1. The Load Balancer (The Scaler) Primary Goal: Availability & Horizontal Scaling. The Job: Spreading traffic across multiple compute resources to eliminate Single Points of Failure (SPOF). The SDE2 Nuance: You need to know the difference between Layer 4 (Transport) and Layer 7 (Application) balancing. L4 is fast; it forwards packets based on IP/Port without looking inside. L7 is smart; it terminates the connection, reads the HTTP headers/path, and routes /api differently than /images. 2. The Reverse Proxy (The Shield) Primary Goal: Security, Unification & Offloading. The Job: Sitting in front of backend servers to hide their topology and IP addresses. The SDE2 Nuance: SSL Termination. Decrypting HTTPS handshakes is CPU-intensive. A Reverse Proxy handles this heavy lifting at the edge, allowing your backend application servers to focus purely on business logic (and communicate via HTTP inside the private VPC). The Reality Check 💡 In modern architecture (and cloud environments like AWS ALB or tools like NGINX), the lines blur. We often use a single component to perform both roles simultaneously—terminating SSL (Reverse Proxy) and then distributing the request to a pool of instances (Load Balancer). But knowing which function you are tuning—and why—is what separates a mid-level engineer from a senior one. Questions for the network: Do you prefer handling SSL termination at the Load Balancer level or strictly on the application server for end-to-end encryption? #SystemDesign #SDE2 #SoftwareEngineering #Scalability #DistributedSystems #DevOps

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The biggest difference between Google and Waymo, almost one year in: Waymo has largely adopted the Amazonian practice of presenting information in documents, not slides. Meetings start with a few minutes of pre-read / frantic commenting on a Google Doc, followed by a live burn-down of the comments that require discussion. It's efficient and information-dense. I love it.

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Imagine you just joined Google as an SWE. It’s your first week, and you’re overthinking everything. You’re surrounded by world-class engineers, new systems, and an overwhelming urge not to mess up. The pressure to prove yourself is real, and so is the anxiety…so what should you do? Over the years, I’ve moved across orgs, teams, and sometimes, entire companies. Here’s exactly what I did (and still do) to ramp up faster and enjoy the process without letting anxiety win → Don’t let yourself stay stuck. If you’re blocked for more than 30 minutes, ask for help. Share what you’ve tried, show initiative, and intent. → Read way more code than you’re assigned. Dive into related modules, historic PRs, and old design docs. The context you gain helps you spot patterns and avoid mistakes. → Ask about the story behind the system. Find out why things are built a certain way, what’s failed before, and what’s on the roadmap. Understanding the “why” beats memorizing the “what.” → Volunteer for unglamorous work. Take ownership of tests, docs, or nagging integration bugs. This is where you quietly learn the most and build your internal network. → Offer help before you’re an expert. Chime in when you see questions, review PRs, or share a script. Even small contributions get you noticed for the right reasons. → Connect with skip-levels and cross-team leads. Set up short intros to learn about the broader vision and team priorities. You’ll spot opportunities to add value beyond your immediate tasks. → Document what you learn as you go. Share notes, update READMEs, or post quick how-tos in team channels. Your future self and your teammates will thank you. → Shadow someone during incidents or launches. Sit in on debugging sessions or war rooms, even if you’re just observing. You’ll see how senior folks think, react, and communicate under pressure. → Keep a running list of “how did that break?” moments. Every time you see a system fail or a hotfix go live, dig in. Reverse-engineering past problems is the fastest way to understand the architecture. → Show up with curiosity and intent, every day. Don’t just tick off JIRA tickets. Look for the bigger picture. People notice when you genuinely want to learn and contribute, not just “do your job.” Some orgs assign mentors, some don’t. Either way, these habits put you in the driver’s seat. Ramping up is less about brilliance, more about consistent intent. And the ones who learn this early end up leading teams later.

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🚨 SWE in 2025 and still ignoring LLMs? You are not just behind …. You are replaceable 💣 But the good news? It’s not too late to catch up — and you don’t need a fancy degree or paid course to start. Note: This is not promotional! I found a goldmine of a FREE resource by Victor Dantas that takes you from: 🔸 Basic Machine Learning ➡️ to Neural Networks ➡️ to RNNs ➡️ to Transformers ➡️ all the way up to LLMs like GPT & LaMDA. It’s literally a zero-to-hero roadmap that breaks down how these complex systems actually work — in plain English. 📚 Whether you’re a SWE, student, or AI-curious engineer — this is the kind of stuff that will future-proof your career. #AI #LLM #MachineLearning #NeuralNetworks #Transformers #SoftwareEngineering #GPT #LearnForFree

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Micron, SK Hynix, and Samsung stocks are soaring but few understand why.   1. The structural reason:   - Memory, in particular High-Bandwith Memory (HBM) has become crucial to run LLMs for billions of users - Running LLMs is mostly is memory-bandwidth bound, not compute-bound - During decode, GPUs spend more time fetching weights and KV cache than doing math, making HBM the primary bottleneck   2. The supply chain reason: - As demand soared, the major players shifted production capacity to high-margin HBM - That led to undersupply of other memory types (SRAM, DRAM) which are still needed for AI   What this means for…   1. Business leaders Memory cost will drive up GPU pricing Even if you don't buy chips, AI infra costs will likely rise as supply chain players will pass on costs   2. Entrepreneurs After decades of silence, there is massive opportunity in innovating memory Its still overlooked by many but we will soon see more high valuation memory startups which will become attractive acquisition targets for the 3 big incumbents   📷: FT   …   Did you find this helpful? ♻️ Repost this to inform your network 🔔 Follow me for more AI insights 🔖  Subscribe to my newsletter

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