The other day, I was reading a fascinating blog post from the LinkedIn engineering team. It was about how they’ve reimagined job search using large language models (LLMs). Reading this only reaffirmed what I’ve believed for a while: search, as we know it, is already over.

We’re entering a new phase—one where people don’t “search,” they express. And systems don’t “match keywords,” they understand intent.

That’s not just clever phrasing. It’s a fundamental shift.

Think about how job search has traditionally worked.

You type:

"React Developer"
"Laravel Developer remote Pune"
"Senior Backend Engineer"

And boom—you get a list of results. Some are good, some are totally off, and others make you question your career choices.

Now compare that to this:

“I’m looking for a remote job as a software engineer, preferably at a startup building something in climate tech. I don’t want to work with agencies. Would love it if it's based out of Bangalore or can pay at par.”

or

“I’ve got a marketing background and want to move into something with social impact. Maybe work with NGOs or environmental orgs. Open to hybrid roles.”

See the difference? The second set isn’t a search—it’s an expression. It’s how a real human talks. And that’s the magic LinkedIn is building: systems that understand what you want, not just what you type.

As someone who's been learning Python and diving head-first into Generative AI lately (read: Demystifying LangGraph: The Backbone of Agentic AI Solutions), I was especially curious about what’s going on behind the scenes here.

Spoiler: it’s not just one giant vector search and a shrug.

What LinkedIn has done is orchestrate a full pipeline:

• The user expresses their intent in natural language.
• That input is parsed, classified, enriched (like converting “New York” to a geo ID).
• Then external context is pulled in—your profile, your past applications, your connections.
• And then it’s turned into a query embedding and passed through a retrieval engine.

This part really got me. We usually think of retrieval as a “vector goes in, matches come out” kind of thing. But when you’re matching across millions of job postings, in milliseconds, that’s just the beginning.

Let’s be honest—just storing text as vectors and doing a nearest neighbor search isn’t enough. Anyone who’s tried building semantic search knows it’s not plug-and-play.

What really stood out to me in the LinkedIn blog is how deliberate they were about how they do vector search.

Instead of using approximate algorithms like HNSW or IVFPQ (which are great for speed but can trade off accuracy), they went with a more brute-force but precise approach: exhaustive search over a contiguous list of document embeddings in memory—basically checking every candidate with GPU acceleration.

It sounds simple, but at their scale, it’s a bold and performance-optimized decision. It shows that accuracy and relevance mattered more than just chasing latency numbers.

This reinforces something I’ve always believed—search quality isn’t just about having fancy embeddings. It’s about how you retrieve and what you prioritize during retrieval.

Retrieval gets you the candidates. Ranking decides what you see.

This is where LLMs shine. LinkedIn uses a distilled cross-encoder model (a “student” trained by a big “teacher” model) to evaluate how relevant each job is to your query. Not just in terms of title, but context, tone, intent, fit.

And they’re doing this with:

• Reinforcement learning loops
• Composite loss functions
• Human + synthetic training data
• KV caching and sparse attention for performance

They’re not just using AI—they're orchestrating it like a symphony.

As I spend more time building AI-powered systems—especially in the world of Generative AI—I keep coming back to this idea:

We’re no longer building interfaces that answer.
We’re building systems that listen and understand.

It’s a subtle but massive shift. One that makes experiences 10x better, especially in areas like:

• Job discovery
• E-commerce search
• Customer support bots
• Even internal tools for teams

This LinkedIn use case is one of the best real-world examples I’ve seen of what’s possible when we blend LLMs, vector search, and thoughtful design.

What excites me about LinkedIn’s implementation isn’t just that it works—it’s that it reflects exactly the kind of experience I believe should define the future of search.

As someone who's been exploring Gen AI, building with Python, and thinking deeply about retrieval, ranking, and user intent, this isn’t new to me—it’s validation. This is the kind of architecture I’ve been studying, prototyping, and aiming to build myself.

The idea that users should express, not just search.
That retrieval should be semantic and contextual.
That ranking must be intelligent, dynamic, and driven by user understanding.

LinkedIn’s system brings all of this together at scale—and honestly, it feels like the blueprint for how modern search should be done.

So if you’re working on search, discovery, or any experience that connects users with what they want—this is your cue. Don’t just index. Don’t just match.
 Understand.