Danny Chen

💡 Standing Out in Tech: Lessons from Lam Research’s Career Webinar by Justin Lai, PMP Lai and Audrey Choi Last week I attended a webinar where Audrey Choi, a former undergraduate student at UC Berkeley obtained a Master’s-level position at Lam Research.   The webinar offered practical advice for students and early-career professionals interested in the semiconductor and tech industry. I came away with insights that I’m excited to apply as I continue developing my skills and projects. Some highlights: ⚡ Projects speak louder than degrees – hands-on experience can really make your resume stand out. ⚡ Be versatile – having multiple complementary skills helps you stand out from the crowd. ⚡ Network strategically – talking to professionals, seeking feedback, and making genuine connections can enhance both your resume and your career path. ⚡ Rejection is part of growth – keep applying, improve your materials, and show continuous development. Events like this are an excellent way to gain practical career advice, learn from experienced professionals, and connect with people shaping the industry. I’m looking forward to putting these insights into practice and continuing to grow in my career! #LamResearch #Semiconductors #CareerAdvice #Networking #ProfessionalGrowth #ProjectsMatter #SkillDevelopment #EarlyCareer

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🚀 🚀 How I helped build a rocket as a software engineer 🚀 🚀 Full Article: https://lnkd.in/gFavf3eY Last year, Shawn Wei Chew and I had the amazing opportunity to serve as software leads for our school’s rocket team, Rocket Project at UCLA. We wrote code for a hybrid propellant rocket that reached an apogee of 19,200 ft, making it the most successful flight in our team’s history. Coming from a traditional SWE background, we didn’t know what to expect, and from our first outing messing with microcontrollers, multi-threading & communication protocols - we’ve learnt tons along the way So we wrote this article hoping that it would be helpful for prospective club applicants, future batches, or at the very least, interesting to the average reader. Enjoy! Acknowledgements - this article (and rocket) would not have been possible without: • The larger Rocket Project organization, who helped build every single other part of the rocket from scratch that was not code — to me, software is tremendously easier than engineering so I don’t know how they do it LOL • Shawn, my fellow co-software lead and one of my best friends, who helped review and proofread this article. • Angela & Yifan, our amazing electronic leads who helped read this article and put out countless of fires when Shawn and I couldn’t • Our two other software leads who helped build this software system with Shawn and I • My wonderful friends who I roped in to build the web app with us • The electronics team, who were all extremely fun to be around and helped us figure out the hardware side of things so I could just write code

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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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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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Everyone’s prepping for SWE interviews these days, but what about AI/ML roles? LeetCode, OOP, system design, it’s solid advice, no doubt. But what if you're not interviewing for a classic software engineer role? What if you're aiming for AI/ML? Because the expectations change. Yes, some things overlap (code quality, problem-solving, communication). But AI/ML interviews bring in a whole other layer: math, theory, model intuition, deployment know-how, and increasingly, LLMs and agents. If you're preparing, here's a real-world checklist that reflects what actually comes up: 📘 ML Fundamentals Brush up on the core ideas: bias-variance tradeoff, underfitting/overfitting, regularization, cross-validation, confusion matrix, ROC-AUC, precision/recall. Understand why these concepts matter, not just the formulas. 📊 Core ML Algorithms Be ready to talk through models like logistic regression, decision trees, random forests, SVMs, and boosting methods. Not just “what” they are, but when and why you’d use them. 🧠 Deep Learning & AI Grasp the role of CNNs, RNNs, transformers, and attention mechanisms. You won’t always need to write them from scratch, but know their strengths, limitations, and real use cases. 📦 LLMs, Embeddings & Agents Increasingly relevant in interviews today: – How does a transformer actually work? – What is RAG and why is it important? – What’s the difference between fine-tuning and prompt tuning? – Use cases for fine-tuning vs. few-shot vs. agentic workflows ⚙️ MLOps & Deployment Expect questions like: – How do you deploy a model as an API? – What’s your CI/CD process for ML? – How do you track metrics or handle data drift? Tools like Docker, FastAPI, GitHub Actions, and RunPod often come up. 🧱 Backend Awareness You might not be writing full-stack apps, but you’ll still need to: – Build and consume APIs – Work with cloud infra (AWS, GCP) – Understand data pipelines and orchestration (Airflow, Prefect) 📌 Bonus: Stay Current LLMs, vector databases, LangChain, agents, Hugging Face spaces, retrieval workflows, know what’s happening, even if you’re not using it daily. Final thought? You don’t need to know everything. But you should understand your stack well enough to reason through your decisions, explain trade-offs, and ask good questions. Good luck to everyone preparing, this field is growing fast, but you're not alone. Would love to hear what you've seen in interviews lately. #MachineLearning #AIJobs #MLOps #LLM #InterviewPrep #DataScience #LangChain #RealWorldAI

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Everyone is hyped about AI. Companies are hyped about AI. Many people are thinking, “Why do we need juniors if AI can do the work?” But the real fact everyone is ignoring is this: Who will become the next generation of senior developers if we don’t have junior developers today? The impact of AI might not look harmful right now, but in the long run, we may not have enough strong senior developers, because many companies are not focusing on the importance of hiring and growing junior developers today. The biggest damage AI has already done is this: it has broken the junior-to-senior developer pipeline.

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Last night, I had the opportunity to return to UCLA to speak in the Philosophy Department’s Being in the World series. It was a full-circle moment. These were the same halls where I first began studying computer science and imagining a career pivot into engineering. We discussed career paths, job markets, AI’s impact on education, and how students can break into tech. But one question stood out: How do you think an undergraduate degree in philosophy has benefitted you both generally and in your field? I’ve thought about that question for years. Generally, philosophy has given me a disciplined way of orienting myself in the world. We are living through profound shifts:  artificial intelligence, ecological destabilization, geopolitical realignment, social fragmentation. Philosophy trains you to examine systems in their totality rather than reducing them to a single domain like economics, politics, or engineering. It provides a scaffolding for moving between disciplines without losing coherence - a space of expansive thought that transcends any particular topic. In my field, philosophy has been equally practical. Software engineering is not fundamentally about writing code. It is about navigating ambiguity, clarifying assumptions, defining terms, and reconciling human intention with technical constraint. At higher levels, engineering becomes the design of logical systems in response to shifting organizational and cultural pressures. The tension between vision and implementation mirrors much older philosophical tensions:  perception and reality, reason and passion, abstraction and instantiation. The questions are ancient. The tooling is new. It has been years since I’ve written on Descartes or Russell, but I remain a philosopher in spirit. Grateful to UCLA Department of Philosophy for the invitation, and especially grateful to the students asking serious questions about their futures.

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I analyzed 20+ job descriptions from Google, Amazon, Meta, Microsoft, Stripe, Figma, OpenAI, Anthropic, NVIDIA, and others to find out what big tech actually wants from new grad engineers in 2026. Here's what the data says: For Software Engineers: → Python (95%), Java (85%), and TypeScript (65%) are the language trifecta → React dominates frontend at 75% of postings → SQL appears in 80% of JDs → Docker (65%) and CI/CD (70%) have moved from "nice to have" to entry-level baseline → AI coding tools like Cursor and Copilot now show up in 40% of postings. For AI/ML Engineers: → Python is non-negotiable (95%) → PyTorch has overtaken TensorFlow as the #1 framework (90% vs 70%) → The GenAI stack is the fastest-growing category, RAG (50%), LangChain (45%), Vector DBs (40%) → MLOps skills (Docker + FastAPI + CI/CD) now separate "shipped" from "studied." → Agentic systems just entered the chat at 30% and are climbing fast Take a look at this artifact for visuals: https://lnkd.in/gtqJgqaZ PS: This is my personal research, and research tools have been used. This analysis is not the industry standard in any manner.

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Stanford paper: young software engineers are the most negatively impacted age group in today's tech job market Employment rates for SWEs in their early to mid 20s are down 20% compared to the peak in late 2022 This study found that: 1. Employment for young workers has declined in AI-exposed occupations, particularly those that involve automating work 2. Overall employment across all sectors continues to grow (just not for young workers - either stagnant or declining) 3. So far AI has led to visible effects on employment rates but not on compensation link to paper in comments

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Early-career engineers: this might be the closest thing to a “golden ticket” into a breakout startup I’ve seen. ​ Alpha by a16z Speedrun is a 2‑month, in‑person SF fellowship that drops you straight into a full-time SWE role (salary + equity) at a fast-growing a16z portfolio company, plus weekly small-group sessions with top founders and investors. You’re an actual employee from day one, not an intern, and matching is mutual, you rank companies, they rank you. ​ If you’ve ever wanted to be dropped into the center of an ambitious startup instead of watching from the sidelines, this is the shot: #tech #startups

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The top 5 highest paying companies for Software Engineers in San Francisco right now. This is median total compensation across all reported salary submissions in SF according to levels.fyi: 🥇 Anthropic | £529k 🥈 Notion | £469k 🥉 xAI | £462k 4️⃣ OpenAI | £450k 5️⃣ Netflix | £417k In San Francisco, the market for top engineers has never been more competitive. And salaries are creeping up all the time. It’s probably no surprise to see frontier AI labs dominating the list. But Anthropic leading the way by such a distance is something I didn’t expect. Makes you wonder: does paying higher salaries lead to better AI models?

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. 🚀 (Feb '26 Recap) What I’m seeing in the AI SWE Job Market right now — Based on conversations with my network and recent market activity, here are some signals: The "AI taking jobs" narrative is hitting a wall of reality. Top-tier AI engineering roles are currently commanding base salaries of up to $500k. The market isn't shrinking; it’s specializing. Specific Patterns + Insights: The Rise of "Agentic" Roles: We’ve moved past simple chatbot integration. From Global Fintech giants to Stealth startups, the hunt is on for "Agentic AI" developers. They want engineers who can build loops where AI actually performs tasks (tool-use, RAG, orchestration) rather than just generating text. The "Return to the Bay" is Real: While remote roles exist for Big Tech contracts, the high-equity "Founding" roles are heavily concentrated in San Francisco and Palo Alto. Over 60% of the people I spoke to this month mentioned being on-site. The "AI Hub" has a physical address. AI + Industry Verticals: We are seeing deep specialization. It’s no longer just "AI." The demand is spiking in Physical AI (Robotics/Automotive), Healthcare AI (Agentic diagnosis), and Private Equity AI (due diligence automation). The "Builder" Sales Engineer: A new breed of role is emerging—Sales/Accelerator Engineers. Companies aren't looking for "slide-deck" presenters; they want engineers who can build Next.js prototypes and technical content to help customers ship AI products faster. Compensation Reality Check (February Averages): - Founding AI Engineer (SF/Seed-Series A): $250k – $500k + Heavy Equity - Senior Data/Infra (Series A/B): $180k – $220k - Enterprise AI Contracts (Remote): $70 – $100/hr How to prepare yourself: - Move beyond the API: Knowing how to call an OpenAI API isn't enough anymore. You need to understand RAG, orchestration frameworks (LangChain/AutoGPT style loops), and latency at scale. - The "Next" Full-Stack: Next.js and TypeScript remain the dominant entry points. If you can pair a polished frontend with an AI-driven backend, you are in the top 1% of the candidate pool. - Systems Thinking: Brush up on RLHF pipelines, data orchestration, and CI/CD. The most valuable engineers move models from "cool demo" to "reliable production system." The Bottom Line: The engineering market isn't disappearing—it's undergoing a massive "re-skilling" event. If you can bridge the gap between traditional software architecture and the new AI stack, you have more leverage today than at any point in the last three years. 🚀 Disclaimer: This is a personal, non-commercial research project based on external market data. Views are strictly my own. #AI #SoftwareEngineering #CareerTrends #TechMarket #FoundingEngineer #Upskilling

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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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Random advice for new CS grads: If you think the entry-level job market in the US is cooked, consider exploring opportunities abroad. From my experience living in Japan for a year, the demand for software engineers is significantly higher, and companies are consistently recruiting new grads. It’s a great way to kickstart your career with less competition and more support. Disclaimer: This path generally works best if you have a bachelor's degree in a related field. Bootcamp grads, I'll share more info soon on how you can break into the Japanese market as well. Don't limit yourself to one market, there’s a world Share this for reach as it might help some new grads start expanding their job search.

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🚨Despite the new $100,000 fee per H-1B visa, Jensen Huang confirmed that NVIDIA will continue sponsoring visas & cover all costs. His reason? “Immigration is central to America’s ability to lead in technology and ideas.” It’s a bold move and a powerful message. Leadership isn’t about cutting costs. It’s about standing by what drives innovation. If you’re a professional aiming for roles in AI or advanced tech, this signals two things: ➝ Top companies will always pay for the best talent, no matter the policy climate. ➝ Your value grows when your skills travel well, when you can adapt, collaborate and build across borders. For founders and leaders, it’s a clear reminder: Don’t let policies decide how you hire or grow. Let your vision lead the way. ➡️Take for students: Build expertise that transcends location. The world’s best companies look for capability, not just geography. 1️⃣Master one domain deeply, go beyond your syllabus. 2️⃣Build real projects and show your work online. 3️⃣Collaborate globally through hackathons or open-source. 4️⃣Focus on solving real problems, not chasing buzzwords. 5️⃣Communicate and teach what you learn, it builds clarity and credibility. Remember: Your strong skills are your passport. They can open doors anywhere in the world.

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