Xueqiao (Joe) Xu

The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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You are in a system design interview at Meta (which owns WhatsApp) for the IC4 role The interviewer leans in and asks: "Why can I log into Discord or Slack on a new laptop and instantly see my old chats, but WhatsApp needs a backup or a long 'restoring history' step? I asked you to design a chat application. How would these two approaches change your system?" Here is how you should break it down 👇 Both WhatsApp and Discord look like simple chat apps. Under the hood, they are built on two very different philosophies. Btw, we’ve recently launched our system design mock interview tool at Layrs. If you’re preparing for a switch or SD interviews, it’s free to use: http://layrs.me/interviews 1. WhatsApp – Phone is the source of truth Idea: Your device is the real database. The server is mostly a courier. What happens when you send a message: – You type the message. – It is encrypted on your phone with keys stored only on devices in that chat. – WhatsApp server receives the encrypted blob, stores it briefly, and forwards it to the receiver. – Once all devices in that chat acknowledge, the server is allowed to drop the message content. – Your long-term history lives in a local database on phone (SQLite style), plus whatever you back up to iCloud or Google Drive. What this means: – New phone, no backup = almost no history. – Multi device is hard, because every device needs keys and a copy of messages. – Server cost is lower, because they are not keeping infinite message history for every user. – Privacy is strong. Even WhatsApp cannot easily scan your chat content, because they do not keep decrypted data. In your design answer, you can mention: + Local storage on device + End to end encryption + Store and forward model + Optional cloud backup handled as a separate flow 2. Discord or Slack – Server is the source of truth Idea: Servers hold a permanent log of messages. Your device is just a window. What happens when you send a message: – You hit send. The message goes to a backend service. – Backend writes it into a big distributed database or log store (for example Cassandra or ScyllaDB). – Indexes and partitions are based on channel, user, time, workspace, so reads scale horizontally. – Any device that logs in simply fetches messages from the server and renders them. What this means: – New laptop, new browser, office desktop, phone, all show the same history instantly. – Multi device is easy, because all devices just read from the same central store. – Server cost is higher. You are paying to store years of chat history and to replicate it. – Privacy tradeoff. Company can run search, analytics, and moderation on server side data. In your design answer, you can mention: + Distributed database for messages + Append only style message log + Search indexes, pagination, and caching + Stronger focus on availability and horizontal scalability Check my 1st comment as well:

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Waymo has unlocked a surprisingly specific use case in San Francisco: parents are trusting it to drive their kids to school. They wouldn’t put their kid in an Uber, but they’ll send them solo in a fully autonomous car. That was the moment it really clicked for me. If AI can handle real-time decisions where safety is on the line, it can handle a mortgage file. Document review, condition clearing, data extraction—it’s not even close in terms of complexity. The challenge isn’t whether it’s possible. It’s whether we’re designing systems that make better use of people’s time—because the manual work isn’t just inefficient. It’s holding the entire industry back. https://lnkd.in/gFVA9Yvk #MortgageTech #Automation #AIinLending

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Super exciting to see reports this morning that Waymo is in advanced discussions to raise $10–15B at a valuation near or above $100B, with Alphabet expected to anchor the round. That would be a major step up from Waymo’s prior ~$45B valuation in 2024 and a clear step change in investor confidence. Notably, Waymo has not raised this year, despite the broader AI funding frenzy. The size and structure of the round also make this feel like a step toward an eventual spinout or IPO. This matters because capital supercharges growth. Waymo is past the “does this work?” phase and firmly in “how fast can we scale?” mode. The company has already logged ~14M rides this year and expects to reach ~1M rides per week by late 2026 as it expands into markets like Miami, Dallas, and Philadelphia, including expanding onto freeways. At the same time, competition and consumer expectations are accelerating. In markets where Waymo operates, fully driverless rides are becoming normal rather than novel. Once people can reliably hail a car with no one in the driver’s seat, tolerance for “pilot programs” and distant timelines drops quickly. The race is no longer about whether robotaxis work. It is about who can scale them first, safely, and everywhere — a win for consumers who get cheaper, safer, and more reliable rides.

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I've been keeping rough track of the number of interviews I've preformed over my career at Google, Facebook and Roblox and I believe I recently passed the 1500 mark!    I’ve talked to everyone from new grads to principal engineers, EMs, directors, and beyond. After so many conversations, you start to notice what really stands out, and it’s not always what you'd expect. Here are three things that consistently make a difference, no matter the level: - Be real. The best interviews feel like a genuine conversation. Talk about what actually happened, the wins, the mistakes, the lessons. Don’t over-polish it. - Stay humble. Great engineers (and leaders) lift others up. They share credit, take ownership when things go sideways, and focus on the bigger picture. - Know yourself. Self-awareness is a superpower. Show how you’ve grown — and what you're still working on. That kind of reflection is way more powerful than a perfect resume. If you're interviewing right now, I hope this helps. And if we’ve had one of those 1,500 conversations.. Thank you! I’ve learned a ton from hearing your stories. #EngineeringLeadership #CareerGrowth #InterviewTips #SoftwareEngineering #AuthenticLeadership

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