Jiafeng Ni

𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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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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A friend of my colleague from Google, who’s based in the US, has just interviewed for the E4 role at Meta. The total package on the table was up to ($260-270k ~ 2.7 Cr) Sharing his interview experience for everyone preparing for interviews right now: 1. Technical screen – pure DSA - 45 minutes - 2 problems straight from Meta’s “Top 25 in last 3 months” on LeetCode - Code could run, but there were almost no test cases - He first explained the approach out loud, wrote it as comments, then coded and walked through examples He cleared this, then got the final loop. 2. Final loop DSA round - Another 45-minute DSA interview - Difficulty very similar to the screen - This time code could not be executed, so clarity of reasoning mattered more than speed - He still did 2 problems, 10 to 15 minutes each, and spent the rest of the time explaining trade offs 3. AI coding round - Given a small codebase with functions to fix or implement - 4 tasks, he fully solved 3 and explained the approach for the last one - AI could help read the code and give context, but not solve details, so fundamentals still mattered 4. System design - 45 minutes - One of the most common Meta questions from HelloInterview - They used Excalidraw, he followed a strict delivery framework and kept asking for feedback as he went - Clear trade offs, scaling discussion, and follow ups on high traffic cases Before we move forward, I also want to tell you about our latest addition to Layrs. The AI based interview tools for system design and DSA are live and free to try: layrs.me/interviews You can run DSA mocks that pick real interview-style questions, get follow-up questions step by step, and get instant feedback on structure and coding. For system design, you can practice delivery on real prompts, draw designs, and get nudges on coverage gaps before you sit in front of an interviewer. 5. Behavioral and the surprise twist - 45 minute behavioral - He under prepared, thought “tech is what really matters” - Feedback after debrief: very strong technical signals, but too many missing signals on behavioral - Meta asked for a behavioral follow up - He then spent a week building strong STAR plus learning stories, did a focused follow up round, and only then cleared into team matching The point is simple. For roles at this level, you cannot treat DSA, system design, AI coding and behavioral as optional sliders. All four show up, and any one of them can decide the outcome.

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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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Building products and systems at Google is complex. But raising three still small kids is objectively harder. Production engineering system is full of redundancy. It is reliable and even has some Up Time guarantees. The system “family” is so fragile and not redundant! Like even though there are two adults but we have pretty different non-overlapping functions - like caregiving or home maintainance (pretty stereotypical.. haha) ? :))) So one needs a lot of **luck** to run the system “family” successfully, and **positive attitude** is a most underrated but most impactful secret ingredient! #WomenInTech #Motherhood #SystemDesign #HumanCentric

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Joakim Recht (Uber Distinguished Eng) on tech leads: "Software engineers need to write code. If you're not writing code, you're not a software engineer." Joakim Recht grew from a Senior Engineer to a Distinguished Engineer at Uber and I asked him what it took to get there. We covered: • The project that grew his career • What makes a great software engineer • Learnings from promo committees • When promos are unfair • Biggest technical mistake at Uber in hindsight • Advice for his younger self 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 𝗹𝗶𝗻𝗸𝘀: • YouTube: https://lnkd.in/gTvsB3Ar • Transcript: https://lnkd.in/gG7bmeU2 • Spotify: https://lnkd.in/g4n-fUxe • Apple: https://lnkd.in/gm8r-2_R

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Google Research Director Denny Zhou, who founded the LLM Reasoning Team at DeepMind, recently gave a great talk at Stanford’s CS25 class: Large Language Model Reasoning. This was one of the most intuitive talks about reasoning model training and application. He outlined new directions for training models to handle questions where answers aren’t easily verifiable. My team is currently working on an enterprise LLM project focused on using reasoning to extract relevant context and generate responses for unverifiable cases. It’s a tough but promising area, and it’s an interesting time to be building. [Link to Denny Zhou's talk: https://lnkd.in/gdJ6Mzi8]

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After the release of iOS 26, I feel confident that AI is not replacing senior engineers any time soon. The progress of LLMs has been remarkable. Scaling compute and data has unlocked surprising capability. There are many well understood tasks that models can now handle, and that will continue to reshape the jobs market. But senior engineers spend most of their time on work that is not well understood. They operate in ambiguity. They make sense of new platforms, new APIs, new constraints. They combine broad experience with intuition to navigate the unknown. Most importantly, their context window grows over years when focused. Over the past few months I used ChatGPT 5 and Claude Sonnet 4.5 inside Xcode while updating Land of Livia to iOS 26. Both often sent me down dead ends with the confidence of a sociopath. Edge cases, platform inconsistencies, silent failures. The models could not reason about a new system based on limited training data. It’s not a matter of scaling data centers. That kind of reasoning requires approaches beyond LLMs. Until then, the frontier still belongs to humans.

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I've interviewed 500+ engineers when i was at Meta. Most candidates obsess over LeetCode and completely ignore the interview that actually determines their level: System architecture. At Facebook, we called it the Pirate interview. LeetCode gets you in the door. But more often than not, system architecture decides your offer level. If you're on the border between Senior and Staff, this is the interview that tips the scale. Most people think it's straightforward: "Design a URL shortener." You whiteboard the textbook answer. Done. Not even close. The real interview starts after that. The interviewer throws a wrench: "Justin Bieber just posted your shortened URL. 10 million people are clicking it simultaneously. What happens to your system?" Or: "This same URL shortener now powers every link the company sends (marketing emails/signups/unsubscribes). If it goes down, growth stops. How does your design change?" This is where experience matters. You need to understand trade-offs that only come from building real systems: For viral traffic, speed is everything. Lower the image quality if you have to, but get that page to load instantly. For business-critical traffic, reliability matters more than speed. Maybe users wait longer, but links must work and failures must be rare. That's what separates senior engineers from staff engineers. Anyone can design a URL shortener. Few can redesign it under real-world constraints in 45 minutes. If you're prepping for FAANG interviews, spend less time on LeetCode problem #247. Spend more time understanding how systems break and how businesses make trade-offs. Past a certain bar, that's the interview that actually matters.

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