Zhen Long

After 8 rewarding years, I bid goodbye to Amazon. I strongly believe that successful companies are built with strong culture at their core, and Amazon’s Leadership Principles are some of the most recognizable ones. I'm grateful that Amazon is where I bootstrapped my career. As I reflect on my journey, I also want to share some personal takeaways that shaped my growth. And of course, in the spirit of “if everything is important, then nothing is”, here goes with my top three. 1. 𝐖𝐨𝐫𝐤 𝐨𝐧 𝐰𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 - You should work on: 1) Thing that matters to your team/org/company. If you are not clear, clarify with your leaders and help define one if needed. What matters? If you work backward from the needs of your customers (those who use your product/service), you will get to it. 2) Thing that matters to you. Ask yourself what motivates you and what can help you learn. Find opportunities that push you closer to what you want to achieve in the long run. If you’re lucky, you get to work on 1 & 2 at the same time. Be conscious of what you work on. 2. 𝐋𝐞𝐚𝐫𝐧 𝐛𝐲 𝐨𝐛𝐬𝐞𝐫𝐯𝐢𝐧𝐠 - Over the years, I’ve been fortunate enough to work with a good set of top talents. I learned the most and the fastest by simply observing how they made decisions at high-stakes moments, how they brought order into chaos, how they knew when to move fast and when to slow down, and how they behaved under high pressure, etc. Try to reason about and internalize those behaviors, and adapt to similar situations you face. Like the phrase “Follow the smart money” in the investment world, go identify and learn from the greats. 3. 𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐠𝐨𝐞𝐬 𝐚 𝐥𝐨𝐧𝐠 𝐰𝐚𝐲 - Career is made up of ups and downs. Confidence is about not shying away from advocating for your work and celebrating your wins. Confidence is also about resilience - learning from setbacks, carrying the lessons forward, and moving on. Confidence is about showing up with energy, curiosity, and enthusiasm in a way that inspires those around you to produce positive results. Be confident, and just have fun! P.S. None of the above, in my opinion, will be replaced by AI anytime soon. :) #growth #day1

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Google+ was wildly successful, just not as a product. I worked on infrastructure teams at Google for a long time. One of the eras I lived through was the rise and fall of Google+, their foray into social. It was not particularly successful as a product, and has since been canceled. Nevertheless, there are some surprising success stories inside what is largely viewed as a failure, and it shapes a lot of how I think about building good infrastructure. The real success of Google+ was its infrastructure. The org had a huge mandate to deliver a sleek and modern social experience across web and mobile platforms, and as they set off to do this, they realized that Google's infrastructure for building new apps was severely lacking. It was hard to spin up microservices and bring them to production. It was hard to monitor huge fleets of microservices and manage their resources. It was hard to build tests that launched a subset of your service stack and mocked the rest. It was hard just to quickly build handlers for HTTP endpoints. There were teams tasked with building a particular webapp with a particular set of functionality. Beneath that, there were teams whose primary job was to make sure that those app teams had a great developer experience, moved quickly, and had world-class tools at their disposal. The output of the first teams ultimately ended up in the trash heap. The output of the latter teams powered Google for the next decade. While the product itself was struggling, the infrastructure sparked a revolution in development velocity for a generation of internal and external apps. Some of the funding poured into the social initiative came back empty-handed, but the infrastructure investments paid long-term dividends. It's not unusual for products to end up in a trash heap. Product development is brutal, and most products fail to find product/market fit. Companies chasing this will thrash their developers around from initiative to initiative, looking for something that clicks with customers. Infrastructure, on the other hand, should have some longevity. I picture these things as a pendulum, or perhaps the tail of an enthusiastic puppy. The product work might go a thousand directions in a minute. They're at the end of the puppy's tail. Infrastructure is the part of the tail anchored to its butt. We can feel that the rest of the tail is thrashing around, but we're not changing direction nearly as fast. That's how I try to approach application infrastructure. My job is to support the development of the product, yes. But I'll also be supporting the next product idea after this one is canceled. As long as the whole company doesn't fail, the tail will live to wag another day. With great infrastructure, the next idea will be half-built on day 1, because all the fundamentals are already in place. Good infrastructure outlasts its motivating use case. I strive to build software that I won't regret building even if the product gets canceled tomorrow.

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If you interview at Google for mid-to-senior roles, nearly every other round tests your system design skills. At Amazon? More than half of your interviews is about system design. At Microsoft? You can expect up to 60% of your rounds to go deep on architecture and design. At Meta? 70%+ of questions will challenge how you build and scale real systems. At Uber? Same story, system design is the backbone of the process. The more senior the role, the more system design dominates the interview, and with AI accelerating everything, even SDE-1s will soon need to master these fundamentals. Here are the most asked questions, so you can start preparing today, which will train you on the fundamentals that are continuously tested.  1. Social Networks & Messaging ↬ Design Twitter Feed ↬ Design Instagram Feed ↬ Design WhatsApp Messaging ↬ Design Facebook Messenger ↬ Design Twitter Direct Messages ↬ Design Instagram Stories ↬ Design Instagram Ads ↬ Design Twitter Notifications ↬ Design Tinder ↬ Design Reddit ↬ Design Facebook News Feed ↬ Design LinkedIn Connections ↬ Design Notification Service ↬ Design Facebook Messenger Payments  2. Media Streaming & Recommendations ↬ Design Spotify Playlist ↬ Design Netflix Playlist ↬ Design Netflix Recommendations ↬ Design YouTube Streaming ↬ Design Spotify Connect ↬ Design Spotify Discover Weekly ↬ Design Instagram Reels ↬ Design TikTok Feed ↬ Design YouTube ↬ Design Amazon Recommendations  3. E-commerce & Marketplaces ↬ Design Amazon Shopping Cart ↬ Design Amazon E-commerce Store ↬ Design Shopify Store ↬ Design Flight Booking System ↬ Design Stock Exchange System ↬ Design Payment System ↬ Design Uber Eats ↬ Design Uber Dispatch System ↬ Design Food Delivery App ↬ Design Uber Pool ↬ Design Uber Freight ↬ Design Booking System (BookMyShow)  4. File Storage, Collaboration & Productivity ↬ Design Google Drive ↬ Design Google Docs ↬ Design Dropbox File Sharing ↬ Design Distributed Cloud Storage (S3) ↬ Design Online Code Editor ↬ Design Code Deployment System ↬ Design File Sharing System ↬ Design Distributed Web Crawler  5. Infrastructure & Distributed Systems ↬ Design Distributed Message Queue (Kafka) ↬ Design Amazon SQS ↬ Design Distributed Locking Service ↬ Design Amazon EC2 ↬ Design Amazon DynamoDB ↬ Design Amazon Redshift ↬ Design AWS S3 ↬ Design Google Analytics ↬ Design Analytics Platform (Metrics & Logging) Check comments for more questions ↓ – P.S: You can find all of these problems on Layrs (layrs.me), It’s Leetcode but for learning System Design, we’re constantly adding more to our list. I quit my job at Google to build this platform and change the way we learn system design for every engineer out there. Also join our Discord Server, we’re quite active and would love to hear from you: https://lnkd.in/g9wGRadq

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🚀 Exciting News: I’ve switched teams within Amazon! 🚀 Many of you have reached out asking about the process of switching teams or organizations within Amazon, and I wanted to share some insights that might help those of you considering a switch. 🤔 Here's what I learned through my transition: 1️⃣ DSA Rounds: For those switching teams, be prepared for 2 rounds of Data Structures and Algorithms (DSA) interviews. The process may vary depending on the organization and team you’re moving to, as well as your previous team discussions. 2️⃣ SDE-2 Level Switch: If you're switching at the SDE-2 level, you should also be prepared for a System Design Interview round. The focus will be on your ability to design scalable and efficient systems, so make sure to brush up on your design skills and experience. 3️⃣ Preparation Tips: Work Summary Doc: Prepare a detailed work summary document that outlines your key accomplishments, contributions, and challenges. It’s a great way to showcase your experience and impact. Design Docs: Be ready with any system design documents or deep-dive docs you’ve worked on, especially if you're making an SDE-2 level switch. The design round can be intense, so highlighting your ability to architect systems is key. 4️⃣ How to decide if switching teams is the right choice for you: Talk to Current Team Members: Reach out to your current team members to understand growth opportunities within your current team. Ask for their suggestions or perspectives on your career progression. Review Your Current Project Plan: Take a close look at your current year’s project plan and evaluate what you’ll be working on. Are the projects aligned with your interests and growth goals? Check Growth in Your Current Team: Assess the overall growth trajectory in your current team. Is there room for you to grow in the next few years, or do you feel you’ve hit a ceiling? 5️⃣ Other Key Considerations: Reach Out to New Team Members: Don’t hesitate to connect with people in the new team to understand their experiences and get a sense of the work culture. Check Code Activity: Look at the team’s code contributions to gauge the complexity and type of work that will challenge you. Review Promotions Tenure: Understanding how long it typically takes for people to get promoted can help you assess career growth opportunities. Discuss Upcoming Projects: Talk to the manager about the team’s roadmap and upcoming projects to ensure the work aligns with your career goals. Switching teams is an exciting opportunity to grow, learn, and take on new challenges. If you're contemplating the switch, make sure you’re well-prepared and do your due diligence to make an informed decision. Feel free to reach out if you want more details or have any questions about the process. 😊 #Amazon #CareerGrowth #TeamSwitch #TechCareers #DSA #SystemDesign #JobSwitch #Opportunities

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I owe a lot to Amazon for the first 5 years of my career. That time gave me a base I still lean on every week: ➤System design habits – Thinking about traffic patterns, failure modes, backpressure, blast radius. ➤Engineering discipline & Mentoring Others – Code reviews, testing, on-call, writing clear docs, closing the loop on incidents. ➤Ownership – If it shipped with your name on it, you stayed till it worked. All of that has helped me much more than any job title. After Amazon, I had to learn a different set of skills, like: - Shipping without a huge internal platform solves half the problems. - Dropping habits that slow you down when you are ten people & not ten thousand. - and many other small things you get used to over months. Both phases were useful in very different ways. So when I see takes like, “Nothing I learned at FAANG helped me build a startup.” I usually think of two questions: 1. How much did you really get to own there - A full project, or a summer intern ticket? 2. What did you choose to carry forward - Good reviews, design habits, writing skills, or only the brand on the badge. Big tech is not a magic stamp for greatness. Startups are not the only place where real work happens. Every environment leaves you with tools and with blind spots. The upside comes from what you keep, what you unlearn, and how you grow beyond the logo on your access card.

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One of the best engineers I worked with at Facebook got promoted from Staff Engineer [E6] to Principal Engineer [E8], making $1,500,000+ per year (more now with stock appreciation). Less than 1% of Facebook engineers are E8. How did he get promoted so quickly? He had an uncanny ability to fix bugs. Not just "this button positioning is off," but "several Staff Engineers spent weeks trying and failing to fix this memory leak." I felt like he could fix anything. Within a year, he became the person that our Director would send into the hairest problems across the org. These were problems that cut across teams, technologies, and APIs, but it didn't matter. Within a few days of dedicated effort, he'd document his learning, and either put out a fix or provide a set of clear instructions to administer the fix. Early-career engineers think that writing code is the most important skill. But debugging is often *more* valuable than feature development, especially at FAANG. Debugging is a premium skill, and becoming exceptional will lead to huge career opportunities. Over time, I learned what made this E8 engineer so effective. I actually credit a lot of my own promo to Staff Engineer from these skills. I documented this from my in-depth debugging guide here: https://lnkd.in/gCDXNZQW

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Want to train LLM to reason right on your mac? Check out my article on Reinforcement Fine-Tuning LLMs with GRPO on Apple silicon using MLX framework. I remembered vividly when DeepSeek-R1 burst onto the scene this year, it dominated discussions over Chinese reunion dinners, AI stocks crashed, and friends I knew (including me) were busy asking it to reason their Bazi. A key idea introduced along with Deepseek R1 was Group Relative Policy Optimisation (GRPO), which helps to incentivise reasoning capability in LLMs via reinforcement learning. Since then, there have been courses such as Predibase's course on implementing GRPO, and improvements to the algorithm such as Mistral AI's Magistral LLM which was trained on a more efficient GRPO algorithm. Following the course by Predibase hosted on DeepLearning.AI, I have written an article where I explain step by step each component of GRPO equation, with a worked example to illustrate the algorithm. If you have on hand an Apple silicon computer, I have also included a Jupyter notebook which implements GRPO using Apple's MLX framework. Credits to Awni Hannun for creating the base notebook for LoRA Fine-Tuning with MLX LM. Full disclosure that I created the article and the notebook with the assistance of Google Gemini. If you noticed any errors with either the article or the notebook, please let me know or file a pull request.

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If you're a Senior Engineer looking to move up, the next role will likely be a Staff Engineer. And in this guide, Shruti shares tips from her own experience of getting promoted to Staff Engineer at PayPal and Slack. She talks about what Staff Engineers do (and how it's different from Seniors), why you might not be getting promoted, and how to take that next step. https://lnkd.in/g58dnFEG

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Amazon Layoffs--------- To everyone impacted by today's Amazon layoffs: I spent nearly 5 years building talent programs at Amazon. I know the culture. I know the people. I know how hard you worked. And I know this hurts. Here's what I want you to hear from someone who's been on both sides of this: This is not a reflection of your performance. I've been in the rooms where these decisions get made. They're about numbers, strategy shifts, and market conditions—not about your value as a professional. You didn't fail Amazon. Amazon's business priorities changed. What comes next: > The Amazon alumni network is one of the strongest in tech. Lean into it. Your former colleagues will show up for you—because they know what you're capable of. > Your Amazon experience carries weight. You've worked at one of the most demanding, innovative companies in the world. That doesn't disappear because of a layoff. To the hiring managers reading this: > These are people who've operated at scale, solved complex problems, and thrived in high-performance environments. >They're strategic thinkers. They're resilient. They're available. > If you're hiring in HR, Operations, Tech, or Strategy—reach out to them. You won't regret it. To my former Amazon colleagues navigating this: > Your network is your safety net right now. Reach out. Ask for help. Make introductions. And when someone offers support—take it. I've been exactly where you are. The job search is hard, but you're not starting from zero. You're starting with credibility, experience, and a network that values you. You'll land on your feet. I'm rooting for you. If I can help—coffee chats, resume reviews, introductions—my DMs are open. UPDATE: I created a free resource to help → https://lnkd.in/g-BtzpKQ #Amazon #Layoffs #CareerTransition #AmazonAlumni #HiringNow

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Why does an E3 level SWE at Meta make only ~190k/year, while an E8 level engineer makes over ~$2M/year, even though both engineers are ICs and spend the same time at work? I have spent the last 5 years at Meta as an IC. I joined with a little over 15 years of experience, and I’ve worked with many solid engineers in this time. Here is how I think about that compensation jump. 1. Same hours, completely different “unit of work” An E3’s unit of work is usually a task or a ticket. An E8’s unit of work is a multi year problem for the company. E3: “Implement this service, fix this bug, write this feature.” E8: “How do we cut infra cost by 20 percent across this product” or “How do we make this platform safe to scale to 10x users.” One person is paid to execute. The other is paid to decide what is worth executing in the first place. 2. Radius of impact E3 usually impacts a file, a service, maybe a small team. E8 shapes whole orgs and product lines. If an E3 ships something great, the impact is great but local. If an E8 ships the right platform, hundreds of engineers become faster and the company saves or earns millions every year. Comp tracks the area of the circle you influence. 3. Risk and downside protection At junior levels, mistakes are usually contained and reversible. At senior staff levels, a bad call can burn tens of millions or damage the brand. E8s are paid for judgment under ambiguity. They decide which bets the company should not make, which migrations can wait, which “shiny idea” is going to kill reliability. You pay more to people whose good judgment protects you from very expensive failures.  4. They scale themselves This can happen in a few ways. 1. Delegation with ownership They define the shape of the problem, then hand large pieces to other senior and mid level engineers while keeping the bar and direction clear. 2. Knowledge that travels They write RFCs, public comments, FAQs, wikis, internal posts. One answer helps hundreds of people who will face the same issue next quarter. 3. Tools over heroics Instead of unblocking people manually all day, they build tools, libraries or guardrails so others can unblock themselves. One well designed tool can save thousands of engineering hours every year. This is what “scaling yourself” actually looks like. The company pays for that multiplier.  5. Ownership of the “uncomfortable problems” Junior engineers usually work inside a well defined box. E8s take ownership between the boxes. They pick up problems that:  Span many teams and no one really “owns”  Require aligning leaders who disagree  Have product, infra, legal and security angles at the same time Most people avoid those because they are messy, political and slow. Very senior ICs lean into them. That is where a lot of value sits. Continued ↓

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David Luan’s Amazon AGI SF Lab marks a significant evolution in Amazon’s AI strategy, distinguishing itself from the company’s traditional AI initiatives. While Amazon has historically focused on scalable infrastructure and consumer-facing products, the AGI SF Lab, under Luan’s leadership, is dedicated to developing autonomous AI agents capable of performing complex tasks across digital and physical environments. The lab’s flagship model, Amazon Nova Act, exemplifies this shift. It outperforms competitors like OpenAI and Anthropic on benchmarks assessing AI agents’ intelligence and aptitude, particularly in tasks involving web interactions . This model integrates reinforcement learning and draws inspiration from robotics to enhance decision-making capabilities, aiming to create AI agents that are dependable and capable of independent actions. Interested to learn more about the AGI SF Lab? Check out the team’s page here: https://lnkd.in/eWHQC7Ps

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