Pei Xiong

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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Localization is more than translation. Pendo.io just rolled out our first localized experience—in Japanese 🇯🇵—designed for Japan’s fast-growing, AI-driven tech market. Go Pendo.io Japan ! From login screens to product areas, we’re bringing product-led growth to users in their language. Learn how we're doing it—and why Japan was first! 👇

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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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Grateful to wrap up an unforgettable summer at Google in the Bay Area. A few takeaways I’m carrying forward: 1. Product management is evolving, fast: After 3+ years in PM, this summer felt different in the age of AI. Market analysis, data insights, prototyping, PRD critiquing, and even building buy-in tools to convince stakeholders now happen more efficiently and quickly, with AI in the loop. PMs need to be full-stack operators. 2. AI is the present: LLMs and agents are reshaping how we build and how people interact with products. In the heart of the Bay Area, and especially inside Google, I saw workflows and experiences being reimagined in real time.  Nearly every experience we use today is up for reinvention. 3. Big Tech at startup speed: I felt a real bias to ship, learn, and iterate quickly. Scale and velocity can (and must) coexist when you’re focused on impact, especially when the playing ground to build and ship is evened out. 4. Network effects are human, too: I learned more from proximity - coffee chats, hallway conversations, and networking events - than from any articles or books. Deep relationships open doors and accelerate growth. Huge thanks to everyone who shared time, feedback, and insights over the summer. Excited to dive headfirst into this new world  🚀

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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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IT HURTS Ask anyone who’s been laid off in tech. And it hurts even more when you start job hunting in the Bay Area. Every second post is about layoffs. Walking through Sunnyvale or Palo Alto lately feels different - the coffee shops are full of founders and engineers rethinking everything. Every job opening gets hundreds of applications. People say, “only the strongest will make it.” But that’s not really true anymore. It’s not about who has the longest resume or who knows one stack perfectly. Today it’s about something else: Adaptiveness. Transition. Reinvention. AI is reshaping roles faster than any previous wave in tech. And yes, that’s uncomfortable. What used to be a specialization is now becoming a moving target. And honestly? That’s the best part! 🔥 Challenges have always been the place where we grow. For engineers and QA, the question is no longer: “What do you know?” It’s: “How fast can you evolve?” The ones who survive this wave won’t necessarily be the smartest. They’ll be the ones who can learn, pivot and rebuild their skillset again and again. Uncomfortable? Absolutely. But this industry was never meant to be comfortable. The good news is that increasing your value in today’s market doesn’t always require years of retraining. Sometimes it’s about high-leverage upgrades that change the game: 🤖 AI-Driven Testing: Not just chatting with GPT, but integrating Codex/Copilot into the IDE to accelerate writing Playwright/Cypress scripts and generating edge-case scenarios. 🤖 Architectural QA: Moving beyond the UI to master API testing (Postman/Newman) and understanding data flows in distributed systems. 🤖 Autonomous Documentation: Leveraging LLMs to transform messy requirements into structured test plans and Gherkin scenarios in secs. 🤖 Shift-Left Mindset: Using AI for early debugging and predictive analysis before a single line of code reaches the staging environment. None of these require burning out for months. But they significantly increase your mobility in the industry. The question today is not: “Will the market stabilize?” The real question is: How quickly can we evolve with it? #Tech #Layoffs #AI #QA #FutureOfTesting #TechResilience #SoftwareEngineering #SiliconValley

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When preparation meets process gaps 🤔 I spent a week preparing for an interview: system design, algorithms, and deep dives into my past projects. Then the first question was about sponsorship. I shared transparently: I am authorized to work in the US for up to 3 years on OPT, and I would need H1B sponsorship in the future. I had also disclosed this during the application. The interview ended immediately. Disappointing, yes. But it reinforced something important Clear alignment upfront saves time and keeps hiring focused on engineering ability and impact. Here is what I bring to engineering teams Scale and reliability 🚀 Scaled distributed systems to 50K plus daily requests at 99.9% uptime ⚡ Improved processing speed by 92% with multi threaded Java pipelines 💰 Delivered 1.2M in annual savings through performance optimization Cloud and modern engineering 🔧 Built production systems using Java, Kotlin, Python, Node.js, Spring Boot, AWS, Kubernetes, Docker, Redis, PostgreSQL 🛠 Strengthened delivery with CI CD, testing, observability, and security hardening, including eliminating 500-plus critical vulnerabilities AI and applied learning 🤖 Built an AI Q and A chatbot using Llama 3.1 on edge serverless infrastructure 🎓 MS Computer Science, AI specialization, 4.0 GPA Leadership 👨‍🏫 Mentoring 200 plus students in Database Systems and OOP, running labs and code reviews Recent builds • Distributed URL shortener with under 50ms p95 latency at 1K requests per second • Crash-safe key-value database in C with 20K plus ops per second • AI powered Q and A chatbot with persistent conversation history I am open to Software Engineer roles and can work on OPT for up to 3 years. For the right long-term fit, I will require sponsorship in the future. For recruiting teams: if sponsorship is not available, confirming it before interviews helps both sides invest time meaningfully. #OpenToWork #SoftwareEngineering #DistributedSystems #CloudNative #AWS #Kubernetes #ArtificialIntelligence #OPT

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Full Stack AI Engineer (FDE) ✅ Quick Summary Location: Onsite in Santa Clara, CA Type: Full-time (Onsite) Visa: Independent only (US Citizens, Green Card holders, or H4EAD L2EAD holders) Openings: 30 positions – suggests a startup or fast-scaling AI company Focus: AI + Full Stack Engineering + DevOps + Data Engineering 🔧 Key Responsibilities Frontend (React - Next.js/Remix) Build modern Single Page Applications (SPAs) Optimize for generative AI workflows and SSE (Server-Sent Events) – likely for live outputs from LLMs Backend (Python, FastAPI) Create microservices with RESTful APIs Implement MCP server/client logic (possibly Multi-Client Processing or something proprietary) Applied AI & LLMOps Build streaming AI servers Work with prompt engineering, guardrails, and Retrieval-Augmented Generation (RAG) Evaluate with BLEU, RAGAS, etc. – metrics for natural language evaluation DevOps Deploy using Docker Compose, Kubernetes, Terraform Cloud automation focus – likely AWS, GCP, or Azure Data Engineering Design and query vector databases (e.g., FAISS, Pinecone, Weaviate) Use pandas, SQL, MapReduce, and graph databases Client-Facing Communicate AI concepts to mixed audiences Translate business requirements into technical implementations 🧠 Required Skills & Experience Full Stack Engineering (React, Python, FastAPI) Generative AI + Prompt Engineering DevOps (containers + cloud infra) Data Engineering (pipelines, vector DBs, etc.) 🚀 Tech & Role-Based Hashtags #FullStackEngineer #AIEngineer #MachineLearning #GenerativeAI #LLMOps #DataEngineer #MLOps #SoftwareEngineer ⚙️ Tech Stack Hashtags #ReactJS #NextJS #RemixRun #Python #FastAPI #Docker #Kubernetes #Terraform #VectorDatabase #SQL #MapReduce #GraphDatabase 🤖 AI/ML Specific Hashtags #PromptEngineering #RAGpipeline #BLEUscore #RAGAS #AIApplications #StreamingAI #AIDevelopment #AppliedAI 📍 Location & Visa-Based Hashtags #SantaClaraJobs #BayAreaTech #OnsiteOnly #USCitizens #GreenCardHolders #EADJobs 🔥 Trending / Broad-Use Hashtags #HiringNow #TechJobs #StartupJobs #AIJobs #EngineeringJobs #BuildWithAI #InnovateWithAI Please reach me at: sathish@ronishit.com

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It all started in a tiny apartment in Tokyo back in 2023. As a software engineer in Japan right now, I am grinding through corporate life while dreaming of Silicon Valley's energy. That dream pushed me to quit and chase my passion for intelligent systems. Hustling remotely at first, I made late-night calls to Bay Area mentors, fueled by instant ramen and sheer grit. That leap immersed me in the Valley's scene vibe, where I poured blood and sweat into next-gen tools for creators worldwide. The early days were raw. Prototypes crashed, potential collaborators ignored my accented pitches, but I pivoted hard. My initial chatbot experiments flopped in tests, so I refocused on code generation, drawing from my own developer frustrations in Japan. Training models on global datasets, incorporating feedback from Tokyo hackers and Valley vets, a breakthrough tool emerged. A demo wowed a key contact: "This triples productivity." From there, I scaled my skills, navigating the fog of uncertainty. Now, in 2025, intelligent systems are exploding, reshaping careers everywhere, from Tokyo offices to Valley garages. I have seen coders triple output with such tools, automating tedium for bolder innovation. Yet, many pros cling to old ways, risking the wave. It echoes my current burnout in Japan, buried in manual code as a software engineer. Here is the playbook from my cross-continent journey: 1. Master prompt engineering. Honed in Tokyo isolation, precise prompts turned tools into my co-pilot, for example, "Build a Node.js API with auth, error handling, and scalability notes." It sparked my first prototypes. 2. Integrate tools like Grok for instant debugging. Bridging time zones, real-time suggestions cut my errors 70 percent, feeling like a global team in one. 3. Automate 80 percent of drudgery. From Japan's rigid workflows, I scripted away data tasks, freeing genius for impact, key to advancing my projects. 4. Adopt tech-first thinking. Daily experiments, fast fails, scaled wins tripled my velocity. I still run hybrid sessions with Japan-Valley collaborators. 5. Network across borders. LinkedIn DMs, virtual meetups, open-source contributions, from Tokyo events to Valley mixers, one chat sparked big opportunities. 6. Embed ethics globally. Bias checks and privacy from day one, respecting diverse users like my Japanese roots, building trust in a borderless field. 7. Measure relentlessly. Track time saved, quality boosts, quarterly tweaks kept me agile through cultural clashes and market shifts. This is my real story: from Tokyo dreamer to Valley-inspired builder, intelligent systems rewrote my path. It can rewrite yours, but act now, the future waits for no one. Grab my guide with templates and case studies through DM. Spots for sharing insights are going fast. Share your story or hurdle below. I will reply.

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