Raman Ahuja

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.

1,037

20 Comments

$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.

210

6 Comments

Ten Years，Three Lessons Today marks my 10th year at Amazon. When I started my career, I never imagined staying at the same company for a decade. But Amazon is not just any company - it's a place that constantly reinvents itself, challenges you to think big, and reward builders. A friend today reminded me: “You only get so many 10 years in your life.” That struck a chord and prompted me to reflect—not just for myself, but also for the younger version of me and the early-career professionals who’ve reached out for advice. I’ll keep it short and share three lessons that have shaped my journey: 1. Choice is more important than effort. I used to believe that hard work alone guarantees success. Over time, I learned that what you choose—your team, your manager, your priorities—can matter even more. Effort is necessary, but making the right choices gives that effort leverage. As we gain experience, we gain the ability—and responsibility—to choose better. 2. Invest in relationships. One of the most important reasons I’ve stayed at Amazon for 10 years is the people. I’ve had the privilege of working with brilliant, generous colleagues who’ve taught me more than any textbook ever could. Some of these colleagues have become mentors, collaborators, and even dear friends. Investing in relationships built on trust, respect, and kindness has made all the difference. 3. You can’t have it all—at least not all at once. Every choice involves trade-offs. Especially as a woman, I’ve had to navigate moments when I couldn’t be fully present at home and pursue every professional opportunity. And that’s okay. We are defined not by what we have, but by what we choose. Looking back, I wish I had taken more risks. I held myself back from opportunities I thought I couldn’t succeed in, without even trying. So for the next 10 years, my goal is simple: be bold, be brave, and choose.

1,023

36 Comments

Despite having 2 years of experience, I failed my interview at Google. Despite years of ML experience, I faced rejections at Google and Roku due to their DSA-heavy interviews. Here are two tough questions I encountered that cost the opportunity 1️⃣ Google ML Interview: Given a massive, infinite data stream of user events, implement an efficient algorithm to identify the top-k most frequent items in real time. 🔹 Challenge: Handling memory constraints and optimizing for speed. 2️⃣ Roku ML Interview: Design a data structure to efficiently support insertions, deletions, and retrieving the median of a dynamically growing dataset. 🔹 Challenge: Achieving O(log N) operations for real-time median calculations. While these questions tested my algorithmic skills, they didn’t evaluate my ability to build, scale, and deploy ML systems which is what truly matters in production. Later, companies like Microsoft and Prezent focused on ML deployment, feature engineering, and system design, leading to my success. If you're aiming for companies like Google and Microsoft, upskill strategically. Don’t just grind LeetCode, focus on both DSA and real-world ML skills. ✅ Master DSA for coding rounds, as some companies heavily emphasize it. ✅ Build, scale, and deploy ML systems to showcase practical expertise in production-ready AI. A balanced approach is key—strong algorithms get you through the door, but real-world ML skills help you thrive. Many ML engineers assume that just knowing ML models is enough but companies want engineers who can deploy and maintain AI at scale. Every company has different expectations, so you need to be prepared for both. Experience alone won’t get you into top tech companies but your skills and domain knowledge will. So, Instead of chasing quick tips, focus on building in-depth knowledge from the basics to advanced levels. One platform I would suggest checking out is Bosscoder Academy. They provide 1-on-1 mentorship from top tech companies which can help you ease your journey. Check their complete program here: https://bit.ly/3FzsmyE They have helped 800+ professionals get placed in top companies. #SQL #DataScience #MachineLearning #collab

408

32 Comments

At 3 AM on a Wednesday, Amazon needed me. I wasn’t even on that team anymore. Back when I was an SDE at Amazon, a Sev-2 had hit one of our services. Many customers were impacted. The team had been chasing it for hours. Still, they called me. I rolled out of bed, opened my laptop, and joined the bridge. The tricky part was that the fault started a week earlier. A code change had shipped behind a flag, traffic ramped slowly, and the failure signature was noisy. Logs were chatty in all the wrong places and silent where we needed them most.Dashboards showed errors, but no clear line to the root. We did unglamorous things. We wrote a minute-by-minute timeline. We diffed deployments, configs, and flags across regions. We checked queue backlogs and retry storms. We sampled logs with new filters. We bisected the traffic ramp. We rolled back the smallest safe thing first. Then we added one log, in one hot path, and the picture snapped into focus. By sunrise, we had the root cause and a fix. The impact was contained. Customers recovered. What stayed with me was not the fix. It was why they called. They did not call because I am smarter. They called because, over months, I had done the boring work. I wrote runbooks when no one asked. I paired with juniors on their first on-call. I shared context so others could make decisions without me. I showed up when it was not my ticket. Trust is not built in one night. It is built in all the small days that come before it. Ownership is simple. If it affects your customer, it is your problem. Org charts are for payroll, not for 3 AM. If you are early in your career, remember this: → Document the weird corners when you discover them. → Add one useful log where it hurts, not twenty where it is easy. → Keep a living checklist for rollback, not a static wiki page. → Teach one person the thing you just learned. → Read your dashboards when things are calm, not only when they are red. At 3 AM, you will not rise to the occasion. You will fall to the level of your habits. Build the right ones while the house is quiet. That’s how you earn trust that lasts beyond teams and ownership that outlives org charts. A few days later, this moment turned into an accolade from the team..  a reminder that trust is built long before it’s tested :)

334

45 Comments

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

100

4 Comments

This software engineer has cracked offers from Google, Amazon, Samsung, LimeRoad, and ION Trading, all within 2 years of graduating, and during one of the worst tech job markets. I recently interviewed Vaibhav Agarwal about his journey. He shared honest insights about his prep, rejections, time management, and the real DSA strategy that helped him win in 2025. Here’s some key takeaways from the podcast:  1. Don’t Chase Every Trend. → Vaibhav tried web, Android, ML/AI, Blockchain, and more, switched stacks every 2 months. → Result? Lots of time lost, nothing mastered. → His regret: “If I had just picked one and stuck with it, things would have been easier.”  2. Pick One Coding Platform Early. → Hopped from HackerEarth, AtCoder, Codeforces, CodeChef, GeeksforGeeks, to LeetCode. → Real progress started after focusing on just LeetCode + Codeforces + GFG.  3. DSA is a Non-Negotiable. → Solved 900+ questions in college, now at 1500+. → Key: Build depth in core topics. → If you’re in 1st/2nd year:  - Keep CGPA above 8, focus on core subjects (DBMS, OS, OOPS, CN).  - Spend 1 month learning one programming language, stick to it for 4 years.  - Learn DSA theory first (arrays, linked lists, stacks, trees, graphs, etc.), then solve problems.  - Use any good LeetCode sheet and do topic-wise, not random, practice. → If you’re in 3rd/4th year and late to the party:  - Prioritize DSA, parallelly revise core CS.  - Don’t get lost in advanced projects; focus on speed, accuracy, and problem-solving.  - Communication skills matter, practice explaining your thought process. Btw, enrollments in my DSA+ Gen AI cohort are still open, check it out: https://lnkd.in/gJQZWGeh  4. DSA Pattern Sheets Make a Difference → “A good sheet covers all patterns and 250–300 core questions.” → Track your progress, do questions topic-wise, and don’t just brute-force random problems. → You can check my DSA sheet: https://lnkd.in/ggZVrwfs  5. Applying is a Game of Patience → Applied to 2000+ jobs, sometimes 30+ times at the same company. → Faced constant rejections, but learned to ignore them and keep going. → Used Naukri, LinkedIn Jobs (sorted by most recent), company portals, and always updated his profile.  6. The Interview Process → 4 rounds:  DSA (harder at Google, standard at Amazon)  LLD (low-level design, open-ended, focus on patterns)  Development (hands-on coding, company tech stack)  Leadership/Behavioral (STAR pattern, team fit)  7. Resume Strategy → College grads:  - Unique projects (not copy-pasted), good contest rankings, highlight DSA and dev skills. → Working pros:  - Show impact, revenue/results, advanced projects, and clear metrics. If you’re a student or early-career engineer, pick one stack, one platform, and go deep. DSA is non-negotiable. Don’t worry about rejections; worry about consistency. What’s your biggest struggle right now? Drop it in the comments, I’ll share tips or bring more stories like this.

538

30 Comments

H1B changes are out of our control. Job search isn’t. It’s been 2 years in the U.S., and take it from me — this post can help you avoid the mistakes I made. I wish someone had warned me earlier. When I first landed here, I believed my college name would be enough. Recruiters would call, and interviews would flow. But reality hit different, and here is what my 2 years looked like: 1. It was my friends at Columbia who first taught me how to actually do cold outreach. Until then, I had no idea that sending those messages, building those connections, would become a lifeline. 2. Without that push from them, I would have been completely lost. Learning: Make good friends, they are your family here! 3. But even with their support, I slipped. With classes, assignments, exams, and a part-time job, I felt busy — but I wasn’t focused on the right things. By the time I realized my mistake, I had already missed out on a summer internship. 4. That was on me, not the job market. Learning: There is no substitute for applying for jobs; it's a numbers game, and I barely applied to 400 internships. 5. So when full-time recruiting came around, I was determined not to repeat that. I applied to over 1,000 jobs, reached out relentlessly (LinkedIn, Email), used every job portal that you can think of, and got call-backs from Meta, Apple, Bain & Company, Amazon, and many startups. Learning: Keep your head down and just apply, email, and send LinkedIn connects. Now, with the new H1B changes, I know many of you are feeling the same uncertainty that I am feeling. And I want you to know: you’re not alone. A lot is not in our control, so let's not waste time thinking about what will happen and capitalize on what we have right now. This time of hiring won't come back again! I didn't always find people to network with when I needed them most, and I don't want anyone else to feel that way. My inbox is always open, so connect with me and feel free to send a message! You've already shown courage by choosing this path. Don't doubt yourself — your story is still unfolding, and there is a happy ending for all of us. #Careers #Technology #H1B #JobSearch #Hiring

1,379

59 Comments

As a founder who want to be in the US for building my company, this is what it meant there is insane panic over the h1b issue, here are the updated facts you should know about - this only applies to the new applications, so if you already in h1b , you are good to go and your renewal will not be changed to 100k, it is the old pricing - if you are already a student studing with f1 visa with otp, you are under old pricing -this fee increase is not annual but is charged per approved petition. - for people who change jobs with transfer, you get the previous pricing, even if you re enter the us , there is no possible change - but if you are coming to study after september 21, aka today, when your employer applies h1b, you are in the new slab , paying 100k usd - same with people coming over directly via h1b and starting a new h1b in the usa from a new employer tldr: people already in the us, either in job or student , there is nothing to worry about, as you are in the old slab for new ones, it is hard as it gets what about founders and startups who want to be in SF for building in AI bootstraping - i want to self fund my h1b , but as a new application, we need to pay the 100k - the better one is O1, which is hard to get, and need to show abilities and funding for the startup - L1, but there is significant infra and process needed for it - or give a 800k - million dollars for EB 5 the only option i see here is get biz upto a million or more, and self sponser this might work for established players, but for startups , if there is a better option, that will help us to build

14

14 Comments

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 ↓

184

12 Comments

Steve Huynh took an unconventional path into tech. He started with a Liberal Arts major yet made his way to Amazon through a friend's referral. After almost 20 years at Amazon he climbed from Support Eng to Principal SDE. Last month, we spoke about his journey and career growth advice, having conducted 1000+ interviews. Here are a few interesting takeaways from our conversation: 1. Why most interview prep advice is garbage – There's too much focus on coding and system design. In reality, coding and system design are just pass no pass. How you present yourself in behavioral interviews has an outsized impact on how you're leveled. 2. Distribution of demand for software engineers is skewed – Demand is limited for junior engineers but extraordinary for Senior+ engineers. Competition exists mostly at the pre-qualification & selection phase, before the interview. He shared some tips on how to be an outlier to compete. 3. Learnings from Amazon's performance-based layoff culture – Now that the industry is operating with increased intensity, it was interesting to hear Steve's perspective coming from Amazon. He shared a bit about how Amazon operates and how to avoid layoffs. 4. Why most engineers don't make it to Principal Eng at Amazon – The requirements for this promotion are almost two levels worth in one. So often engineers get blocked because they start taking on Principal behaviors only to be told they need to contribute more code. Steve shared why his promotion got blocked a few times. 5. What it was like becoming a SWE before Leetcode – There's more competition now and more resources. Back then, there wasn't anything like Leetcode, so technical interviews were much less standardized. Steve shared about how interviews used to be. If this sounds interesting to you, you can watch / listen to the full conversation anywhere you get podcasts: Youtube - https://lnkd.in/gsRCp-JW Spotify - https://lnkd.in/g8dUKg79 Apple Podcasts - https://lnkd.in/gVqcNU4Z Also here's the trailer, I put a bit more work into it this time curious to hear what you think:

328

15 Comments

Today marks my last Day One as an intern at Amazon. In my internship as a Technical Program Manager I stayed close to science and data while owning the clarity of the problem, the path to a decision, and the rhythm of delivery. I spent my days working backward from the customer, sitting with scientists and engineers to understand assumptions and edge cases, then translating that understanding into plans that teams could execute without noise. I learned to go deep before I decide, to write so that alignment happens even when we are not in the same room, and to move with calm speed when information is incomplete but the window for action is narrow. Three lessons I am taking forward 1. Technical fluency changes outcomes. Reading the notebooks and docs, reproducing a baseline once, and asking about leakage, drift, and failure modes is not a nice to have for a TPM, it is the mechanism that earns trust and de risks scope. You do not need to own every line of code, but you do need to own the questions that reveal whether a solution will hold in production and under what conditions it will fail. 2. Decisions need a durable record. A one page decision memo that lists the options, the customer impact, the risks, the guardrails, and the chosen path beats weeks of memory based debate. Share it early, invite disagreement, capture the dissent, then commit and measure. This will simple habit prevents scope drift, reduces rework, and makes handoffs clean because context travels with the work. 3. Translate research into small end to end slices with real signals. Break big ideas into the smallest user visible thread that can be tested, define success with observable metrics, and pair every milestone with a demo and a short write up. When progress is visible and the customer signal is clear, funding is easier, trade offs are explicit, and quality rises because the team can insist on high standards without losing speed. I am grateful for the people who challenged my thinking and shared knowledge with generosity, and I leave more curious, more precise, and more confident about building. Excited to see what comes next. See you in the next chapter.🚀

732

8 Comments

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?”

545

28 Comments