Nursultan Amir

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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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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You are in a system design interview at Google for the L5 Senior Engineer role, and the interviewer leans in and asks: “Why does Spotify keep playing when I drive into a tunnel with no signal, but YouTube Music often stops or buffers? If you were designing a music streaming system, what different design choices would lead to these two behaviors?” Here is how you break it down. 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 We’ve added new features as well:  -Company-specific interviews -In-built interview scheduler  -performance insights & trends — Both apps look like simple music players. Under the hood, they are optimized for very different priorities. [1] Spotify style – Cache first, stream second Idea: The client behaves like a smart offline player that happens to stream. The backend is built to support aggressive prefetching. What happens when you hit play - Client requests the track from a CDN or edge node. - Instead of tiny chunks, it downloads a big buffer ahead of the playhead. - In parallel, it starts pre downloading the next few tracks in the queue. - Data is written to an encrypted cache on disk, not just memory. - Playback reads from that local cache, not directly from the network socket. What this means for tunnels and bad networks - When the network drops, the player already has tens of seconds or entire tracks cached. - Because the next one or two songs are already downloaded, you can be offline for a while and never notice. - Cold start cost is a bit higher. First play might take slightly longer, but then everything feels smooth. - It burns more local storage and possibly more data, because not every prefetched song will be listened to fully. In a design answer, you can mention - Local disk cache with eviction policies (LRU per user, per device). - Background prefetch of N upcoming tracks based on queue. - Download manager that adapts how aggressively it prefetches based on network quality and user settings. - CDN tuned for larger object delivery and range requests. - Explicit offline mode that pins playlists into cache. [2] YouTube Music style – Stream first, cache is minimal Idea: Treat audio like video streams. Cost and bandwidth are optimized first. When you hit play - Player requests audio (and sometimes video) via HLS or DASH-style chunks. - Each chunk is only a few seconds long. - Client keeps a small rolling buffer in memory, not a large queue on disk. - Prefetch of future songs is limited, because video tracks are large and expensive to fetch speculatively. What this means for tunnels and bad networks - If your connection dies, the player only has a few seconds of buffered data. - As soon as those chunks are consumed, playback stalls. - Startup can feel snappy and data usage is controlled, especially for casual listeners. - Works very well on stable networks, feels fragile in spotty coverage.

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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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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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This was a good discussion about the Principal Engineer role at Amazon! I often say that the Principal Engineer role as it exists at Amazon simply doesn't make sense at smaller companies. To many, the title just means the next highest level of engineering skills. At Amazon, it requires dealing with high levels of ambiguity, business strategy, and organizational influence. If you're at a company with <50 people, the person with that level of influence should be leading the whole technical organization - it requires a significant amount of scale before it makes sense to have an IC role for it. I also personally appreciated Steve's points on internal level progression - or as Gergeley says, "Why being promoted from Senior to Principal at Amazon is one of the hardest jumps in tech." Steve: "Basically, to get from senior to principal, you have to do like two and a half level jump, from L6 to L7. Technically, it sounds like one level, but at some other companies, this might be like, you know, L8, L9 or L8 and a half. [...] I noticed that some of the best engineers that I'd ever worked with were having such problems getting to principal engineer that they ended up moving to Facebook or to Meta or to all these other places where the progression was sane. Now they're senior staff and, you know, principal and distinguished engineer at other companies. And so because we had high standards, we actually had this brain drain." In addition to the level itself being a big jump the promotion process is notoriously arduous and arbitrary. While I was L6, I had multiple managers over several years acknowledge that I was doing L7 work, and even agree that if I was being hired in from outside I would be hired at L7, and it was the internal promotion process that was the barrier. This was not the *only* reason I left Amazon, but it certainly made it very easy to do. Amazon has lost a lot of skilled engineers this way, but it's a difficult problem to fix, since the incentives are fundamentally badly aligned. If an L6 engineer is already doing the work that's needed, it's very hard for a manager to justify spending the 100+ hours of work it takes to advocate for their promotion. And while there have been attempts to make the process less arduous, no one wants to be seen as "lowering standards." I highly encourage engineers in that position to make it clear they will not be continuing to do work that way indefinitely. And I encourage leaders at other companies to learn from the example! https://lnkd.in/guC35n2B

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🦺 🛟 Agent Safety > Model Safety LLMs didn't break your app. Agents can. As we wire LLMs to tools, browsers, payments & code the risk shifts from bad text to bad actions. Top failure modes (seen in the wild & in papers): 🚩 Prompt/Content Injection -> Tool Misuse. Malicious web pages or data instruct agents to steal secrets or make unintended API calls. This is now a primary risk for browsing and MCP - style tool agents. 🚩 Over permissioned tools & long-lived tokens. Agents get "god mode" scopes. A single injection becomes account takeover or irreversible ops. Security notes for MCP emphasise least privilege and short-lived auth. 🚩 Unsafe web autonomy. Benchmarks show web agents will attempt harmful tasks unless constrained (posting misinformation, illicit sales, etc.). You must measure misuse not just assume guardrails. 🚩 Supply-chain & retrieval poisoning. Agents trust plugins, third-party tools & indexed data that an attacker can taint. OWASP’s newest GenAI Top 10 calls this out explicitly. 🚩 Process safety gaps. NIST's GenAI Profile (AI 600-1) warns that organizations ship agents without role clarity, human-in-the-loop or incident playbooks. ✅ Model safety reduces bad text. Agent safety prevents bad state. Treat the agent like a junior SRE - with narrow permissions, audited actions and clear escalation paths. If you're building agents, what's the one control you won't ship without? Also drop a comment if you'd like me to share a follow-up post on how to tackle each of these risks. #AgenticAI #AISafety #AIAgents #GenAI #CyberSecurity #MCP #OWASP #NIST #AITrust #ResponsibleAI #AIForBusiness

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The Waymo Blackout Is a Systems Engineering Lesson The recent service disruption at Waymo is being discussed as an “autonomy issue.” That framing misses the real point. It is a distributed systems and reliability problem operating in the physical world. The engineering standards is significantly higher for these systems since... 1) Failure handling becomes a user-facing feature 2) Centralized intelligence improves safety but increases coupling 3) Reliability is measured in trust, not uptime alone In safety-critical systems, the correct decision is often to stop. That can look like an outage from the outside, but it is frequently evidence of conservative, responsible engineering. Incidents like this are not setbacks. They are case studies in what it actually takes to build production systems where software meets physics. What do you think is the possible solution to handle these edge cases? if(txPower == 0) ? Scenario when there's a power outage if(txPower == Intreger.MAX_VALUE) ? Scenario when lightning cable strikes the vehicle #AutonomousVehicles #SystemsEngineering #DistributedSystems #ReliabilityEngineering #SafetyCriticalSystems #Robotics #AIEngineering #Infrastructure #SoftwareEngineering Media Source: YouTube

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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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Over the past year, my team and I built Pinterest's Model Context Protocol (MCP) ecosystem from the ground up: a central registry, a growing fleet of domain-specific MCP servers, and production integrations across our IDEs, internal chat surfaces, and AI agents. MCP is an open standard that gives large language models a unified way to talk to tools and data sources. At Pinterest, we used it as the substrate for AI agents that can safely automate real engineering tasks, from querying Presto data on demand to diagnosing Spark job failures and surfacing institutional knowledge. A few things I'm particularly proud of: * We designed for security from day one: two-layer auth (end-user JWTs + mesh identities), business-group-based access gating, and mandatory human-in-the-loop for sensitive actions. * We made it easy for any team to ship a new MCP server by creating a unified deployment pipeline that handles all the infrastructure, so domain experts just write business logic. * The ecosystem has scaled to 66,000+ invocations per month across 844 monthly active users, saving an estimated 7,000 engineering hours per month. This was a cross-functional effort. Thank you to everyone on the Agent Foundations, Security Engineering, and Traffic Engineering teams who made this possible, and to our engineering sponsors for their support and guidance. Full write-up on the Pinterest Engineering Blog: https://lnkd.in/e-mH26Bw

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My Startup Was Working… Until It Wasn’t We had paying customers. We had local press. We were gaining traction. It was 2016. Then UberEats, DoorDash, and Postmates entered Portland with billions in funding. At the same time, Intel—where I worked full-time—announced massive layoffs. Overnight, my 40-hour week almost doubled. Midnight fab shifts, weekend work, endless overtime. My startup didn’t die because the idea was bad. It died because I didn’t have the financial runway to compete. Here’s what that painful chapter taught me: 1. Passion + grit aren’t enough. Startups need time, capital, and breathing room to survive. 2. Corporate is just built for stability. It can’t nurture the creative chaos that startups need. 3. Adaptation beats stubbornness. Sometimes the smartest move is to regroup, save, and come back stronger. So I made a choice: doubled down on my W-2 career, moved to Seattle, and joined Amazon to maximize income. Every dollar earned—and every loan I could pull—went straight into real estate. One property a year turned into two… then into double digits in a single year. Deals kept coming, I kept closing, and I built a local team to keep momentum alive. By the end of 2022, my portfolio was generating more passive income than my paycheck. That’s when WoolooAI.com was born—not as a flashy startup, but as a necessary system to simplify and automate the chaos: acquisitions, CRM, portfolio management, project management, all streamlined. The truth is: - Without the grind of my early startup days, I wouldn’t have landed the Amazon role. - Without Intel’s layoff threats, I wouldn’t have learned the importance of building a financial runway first. - And without the expertise I developed at Amazon, I wouldn’t have gained the skills that helped me build WOOLOO AI. The irony isn’t lost: the same challenges that once crushed me ended up shaping the freedom I enjoy today. 💡 Takeaway: Sometimes what feels like an ending is really just the foundation for the next chapter. 👉 If you want the full story, I wrote it here: https://lnkd.in/geEVye36 💬 I’d love to hear your story too—what twists or setbacks ended up shaping where you are today? #Entrepreneurship #Startups #RealEstateInvesting #AmazonAlumni #FinancialFreedom #LessonsLearned #HumbleGrowth

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