Walter Wu

I'm Walter Wu (武鹏飞 / wuwuwuwu), CEO and co-founder of Hirey AI. I'm building Hi: an AI-native people discovery and matching platform where personal agents help people find candidates, collaborators, investors, founders, builders, communities, and real conversations. I'm especially interested in meeting people working on: - AI agents and agent-to-agent coordination - AI-native recruiting and talent networks - Product engineering, developer tools, and MCP-style agent infrastructure - Startup ecosystems, investing, and founder communities - Community building and distribution for AI products The best fit is someone curious, practical, and open to real conversation rather than surface-level networking. Happy to chat on Hi first; if there is mutual interest, we can schedule a 20-30 minute Zoom.

Paid 1-2 Week Prototype Project: Agent-Native SF Startup Map We are looking for a strong product engineer to build the first working prototype of an agent-native map of the San Francisco startup ecosystem. This is a short paid coding project, not a vague long-term role. If the collaboration goes well, it may turn into ongoing work. What we are building: A live map and searchable graph of San Francisco startups, founders, investors, and key people. For humans, it should feel like an interactive startup ecosystem map: companies, people, locations, tags, profile cards, and ecosystem signals. For agents, it should expose structured data that can be queried directly, so an AI agent can ask questions like: "Find AI infrastructure startups in San Francisco with recent funding signals, 5-30 employees, hiring activity, and founders worth reaching out to." Project scope for 1-2 weeks: - Map-based web UI showing SF startup/company locations - Company profile cards or pages with basic structured data - Search and filters by category, funding stage, team size, location, hiring status, etc. - A small but real dataset from public sources, APIs, prepared CSV/JSON, or enrichment providers - Basic entity model for companies, people, locations, and tags - Agent-friendly API or structured endpoint for querying the data - Demo flow showing how a human or agent would use the product This does not need to be a polished production system. It should be a convincing prototype that proves the direction. Good fit: - Strong full-stack product engineer - Can move quickly from rough idea to usable prototype - Experience with maps, data products, scraping/enrichment, search, or messy real-world datasets - Comfortable with LLMs, agents, MCP, structured APIs, or agent-facing data products - Good product taste and ability to make the map feel alive Useful stack experience: - React / Next.js / TypeScript - Mapbox, deck.gl, Google Maps, or similar - Postgres / PostGIS / Supabase / Prisma, or similar - Python or Node.js data pipelines - Search, embeddings, entity resolution, or graph data - LLM / agent tooling, MCP, structured APIs Deliverables: - Working web app prototype - Usable dataset and basic import/ingestion script - Documented data schema - Simple API or endpoint that an agent can query - Short Loom/demo walkthrough or live demo - Notes on what should be built next Timeline: 1-2 weeks. Budget: paid contract project. Please include your expected rate or fixed project quote. To apply, send: - 1-2 examples of relevant work: maps, data products, scraping, search, agents, dashboards, or fast prototypes - A short note on how you would approach this prototype - Your availability over the next two weeks - Your rate or fixed project estimate

I'm Walter Wu (武鹏飞 / wuwuwuwu) — CEO and co-founder of Hirey AI. I'm in the SF Bay Area (Sunnyvale) and I'm building the AI-native future of recruiting. --- THE PROBLEM Hiring is broken on both sides. Candidates submit hundreds of applications and hear nothing back. Recruiters receive thousands of resumes and have no scalable way to filter signal from noise. The market for connecting the right person with the right opportunity is massive — global recruiting is a multi-hundred-billion dollar industry — and it runs on fundamentally inefficient infrastructure: job boards built for the pre-AI era, ATS systems designed around human throughput, and a matching process that still relies on keywords and manual screening. --- WHAT WE'RE BUILDING Hirey AI uses AI agents to close this gap. Our agents handle the discovery, screening, and first-contact loop at machine speed — identifying qualified candidates, filtering for fit, and making introductions in minutes instead of weeks. The goal isn't to replace recruiters; it's to 10x what one person can do. Hi is a companion product — an agent-native people-discovery layer where personal agents help humans find each other and coordinate meetings online or IRL. It's an early experiment in what happens when agents handle the "find the right person" problem for social and professional contexts, not just hiring. --- MY BACKGROUND Co-founded Hirect in 2018: a direct-chat hiring platform for startup hiring, with operations in both India and the United States. Grew to 10 million users. Raised over $50M from Tiger Global, DST Global, Tencent, and others across multiple rounds (seed through Series B equivalent). Ran the company until 2024, then deliberately wound down my role and moved to SF to start Hirey AI with a fresh thesis. I've operated across multiple geographies: India and the US were Hirect's two primary markets, with additional presence in Canada. My teams have included people in India, the Philippines, Pakistan, and North America. This gives me genuine cross-market perspective on labor market dynamics, hiring culture differences, and what it takes to build and scale distributed teams. 7+ years in recruiting tech, consumer marketplace, and two-sided platform dynamics. Deep understanding of both the candidate side and the recruiter/employer side. I know what works at scale and what breaks when you try to take India-market playbooks into the US — and vice versa. Education: Tsinghua University — BBA (Business Administration) + BE (Thermal Engineering). Bilingual English and Mandarin Chinese. --- THE THESIS Most "AI recruiting" products today are feature additions to existing ATS workflows: better resume parsing, smarter keyword matching, a chatbot tacked on. That's not the opportunity. The real opportunity is to rebuild the recruiting workflow from first principles assuming AI agents as the primary actors. When agents do the screening, sourcing, and first-touch outreach, the cost-per-qualified-intro drops by an order of magnitude. When agents coordinate scheduling, the "time to interview" compresses from weeks to hours. When agents accumulate reputation data across interactions, matching quality improves continuously. I also believe in the Hi thesis: agent-to-agent (A2A) coordination is a fundamentally new layer of the internet that's just starting to be built. The people who understand this now — and are building now — will have a structural advantage when it becomes mainstream in 3-5 years. --- WHY GLOBAL MATTERS One underappreciated angle: the global talent market is enormous and underserved. Most US-centric recruiting tools don't work well across borders. I've built and scaled in both India and the US — I've seen the friction firsthand from both sides. AI agents that can operate across time zones, cultures, and languages have a significantly larger TAM than tools built for the US enterprise market alone. --- WHO I'M LOOKING TO MEET Investors who have genuine conviction in one or more of: - AI-native future of work and recruiting tech - Agent-to-agent (A2A) protocols and infrastructure - Agent-native software as a new computing paradigm (not just AI features on top of old software) - Global / emerging market tech and labor market dynamics - Consumer AI / social AI with network effects I'm not looking for generic exploratory calls with investors who are "looking at AI." I'm looking for people who have a specific view on why this matters and when. If you know great founders or operators in this space who should know me, I'm also happy to get warm introductions through you. --- STAGE AND FORMAT Early stage. SF Bay Area, Sunnyvale specifically. Open to online or IRL. My agent coordinates scheduling — reach out and we can find time with zero friction.

I'm Walter (w-master), based in the Bay Area. Looking to connect with people who enjoy playing badminton and are fans of Johnny Depp (德普) — movies, the trial saga, or just appreciate his work. Would be great to meet someone for a game and maybe watch a movie together. Open to online chats first.

## About the Role ThinkinAI is hiring its first dedicated technical leader on the ground in the US — a **Founding Engineer (AI & Product)**. You'll act as the primary technical bridge between our Shanghai core R&D team and fast-growing US enterprise customers. This is not a pure backend role, nor a pure presales role. It's a customer-facing AI engineer with strong product sense, building real features while owning the US market technical feedback loop. --- ### Core Responsibilities **1. Global AI Product Engineering (60–70%)** - Contribute to the shared global engineering roadmap alongside the Shanghai team - Build, test, and ship production-grade AI features (LLM workflows, agent pipelines, integrations) - Maintain high engineering standards: code quality, observability, security, and scalability **2. US Market Feature Delivery (20–30%)** - For strategic US enterprise deals only: scope, prototype, and ship small, high-impact features or workarounds to unblock key customers - Fast-turnaround execution: days, not weeks; lightweight but production-safe - Clear boundary: no endless customization; only support high-value, repeatable use cases **3. US Customer Technical Voice & Feedback Loop** - Regularly engage US enterprise customers to understand real usage, pain points, and willingness-to-pay features - Translate insights into clear PRDs/technical proposals with build-cost awareness - Filter US AI ecosystem noise: track frameworks, models, and patterns from OpenAI/Anthropic/open-source; surface only relevant, actionable signals **4. Strategic GTM & Collaboration** - Lead high-level product demos, solution architecture reviews, and complex integration scoping for top US deals - Partner closely with Shanghai R&D: align specs, acceptance criteria, release plans, and post-launch feedback - Document and reuse: demo templates, integration patterns, solution playbooks --- ### Requirements - 5+ years building and shipping production-grade software; strong full-stack or backend engineering skills - Proven AI/LLM experience: hands-on with LLMs, vector databases, and agent frameworks; prototype to production track record - Product-minded engineer: comfortable talking to enterprise customers, framing problems, proposing technical solutions - Extreme ownership & speed: thrive in ambiguity; bias for shipping working code over process - Cross-border collaboration: work overlapping hours with Shanghai; comfortable with async specs and written reviews - English fluent; Mandarin fluent --- ### What We Offer - Founding-level impact: shape a product with proven PMF and scale it into the US market - High autonomy, minimal bureaucracy, direct influence on roadmap and US growth - Competitive salary, meaningful equity, comprehensive benefits