The xiaoren are not giving up! DeepSeek sees itself as a company that is building AGI. What has changed was the scale and the maturity of the AI stack. If you read these job postings, you get the feeling for what they're building. Yes, agents, but it's a bit more… longtermist.
🚀 DeepSeek’s hiring wave signals a turn from model lab to product company Zhihu contributor 锦恢 reads DeepSeek’s plan to double every department as more than a normal hiring push. His view: DeepSeek is changing how it sees itself. It is no longer just a research-heavy model team. It is starting to look like a company that wants to build products, shape user habits, and push AI into everyday workflows.
🔄 Research alone does not change daily life In the past, DeepSeek looked like a large-model research unit attached to a quant company. Its focus was theory, model validation, and technical breakthroughs. The public-facing model service felt more like a spillover from strong engineering talent. But the author argues that real-world impact often comes from products, not only theory. In AI, many important shifts are no longer just papers. They are product patterns. Think of skills, MCP, AGENTS.md, coding agents, Claude Code, Codex, and the workflows they create. These products train the market. They teach users what AI can do. They also define new habits.
🧭 DeepSeek may now want product-level influence That is why this hiring plan matters. If DeepSeek wants more people to feel the power of AI, it has to build products. Models are the foundation. But products are how ordinary users touch the model. The author’s core judgment is clear: DeepSeek is moving from a research institution toward a product company. That does not mean research becomes less important. It means the next stage may be about turning research into usable systems. And that may create wider influence than theory alone.
🧑💻 The job list says AI coding still needs engineers One detail in the hiring post is very telling. DeepSeek is still hiring normal frontend engineers, backend engineers, and testing engineers. It is not pretending that one “AI full-stack engineer” can replace a real product team. That is a sober signal. DeepSeek seems to understand where AI coding is today. AI can write code. AI can speed up engineers. But commercial-grade products still need professional software teams. They need frontend quality. They need backend reliability. They need testing. They need people who can ship and maintain real systems. So this hiring page is also a quiet answer to the “programmers are no longer needed” narrative.
🤖 Agent Harness is the most important role to watch The brightest signal is in the Agent-related roles. The first is Agent Harness. This is about the structure around autonomous agents. It includes agent design, tool arrangement, task flow, and the engineering system that lets agents work reliably. If DeepSeek builds its own Agent Harness, it could quickly move toward a product in the same category as Claude Code or Codex. That would be a major step. DeepSeek would no longer only provide a model. It would provide a working agent product.
🧱 Agent Infra points to platform ambition The second role is Agent Infra. This concept is still new, but important. It usually points to the infrastructure behind agents: agent frameworks sandbox systems tool runtimes execution environments workflow orchestration This is not just a feature layer. It is the layer that makes agents stable enough for real use. The author notes that Agent Infra may be more suitable as a company’s middle platform than as a standalone startup project in China. Why? Because the value is deep, but not always immediately visible. It takes heavy investment. It takes patience. And it supports many products behind the scenes.
🛠 DeepSeek has a product gap to close The author gives one concrete example. DeepSeek’s own website previously pointed users toward Claude Code for agent access. But newer Claude Code versions had already blocked DeepSeek. From a product experience angle, that is awkward. It shows a gap. DeepSeek had strong models, but not yet a complete first-party agent product. In the past, this was acceptable. DeepSeek did not position itself as a product company. But if it now wants to build user-facing AI tools, this gap has to be closed.
🧪 AI Builder roles show another direction The author also highlights the AI cross-disciplinary research roles. They resemble the “AI Builder” roles seen in Silicon Valley startups. These people are not just traditional researchers. They combine product sense, technical ability, and fast prototyping. One person, working with AI, can analyze a need, build an MVP, test a use case, and explore whether a new workflow is worth scaling. The author calls them a kind of product scout. They help find new AI-native use cases before the larger product team moves in.
✅ What this hiring wave really means DeepSeek’s expansion is not just about headcount. It suggests a larger shift in strategy. The company already has strong base models. Now it may be building the product, agent, and infrastructure layers around them. That means: commercial software teams coding agent products Agent Harness Agent Infra AI-native MVP exploration cross-domain product experiments The deeper signal is this: DeepSeek may be moving from proving model capability to building AI systems people use every day. That is a different kind of competition. Benchmarks win attention. Products win habits. And habits are how AI becomes infrastructure.
🔗 Full analysis: https://www.zhihu.com/question/2053625552725079594/answer/2053824533614875163
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