What it's all about
You've Mastered Traditional Search. You Know What's Coming Next.
You know Elasticsearch inside out. You've optimized SQL queries until they sang. You've built MongoDB aggregation pipelines that handle millions of documents. You understand inverted indices, query parsing, and relevance scoring.
But you've also noticed something: keyword matching has limits. Users don't always search the way your indices expect. The gap between what people mean and what they type is where traditional search breaks down.
And You've Seen What's Replacing It.
You've read about vector databases. You've seen demos of semantic search that actually understands intent. You know this isn't hype – it's a fundamental shift in how search works.
The problem? Your current company isn't building this. Maybe they're "evaluating"it. Maybe it's on some roadmap for next year. Maybe they don't see it at all. Meanwhile, every quarter that passes, engineers elsewhere are gaining production experience you can't get.
This is especially true in some European countries. Many companies here are cautious, slow to adopt, waiting to see what others do first. If you're stuck in that environment, you're watching a transition happen without being part of it.
This role exists to solve that problem.We're hiring experienced backend engineers with strong database foundations and giving them the projects to make this transition – with support from a team that's already done it.
What We're Building.
At JobLeads, we match millions of job seekers with opportunities across 40+ countries. Search and matching are at the core of everything we do.
We're actively complementing traditional keyword search with semantic architectures in a wide variety of use cases. This isn't a research project or an innovation lab – it's production systems, real users, measurable results.
Two Dimensions of AI at JobLeads.
This role is about both:
- Building AI-powered search systems
- Transforming how you write software
- The shift you've sensed is real:
- Your SQL and Elasticsearch skills aren't obsolete – they're the foundation.
- We'll help you build the semantic layer on top.
What You'll Be Doing:
Month 1: You'll own a component of our search pipeline. You'll learn embedding strategies, vector indexing, and retrieval optimization hands-on – with mentorship from people who've built this at scale.
You’ll work with:
From keywords to meaning: Embedding models that understand intent, not just text matches
Vector databases at scale: Storing and querying millions of embeddings with sub-second latency
Hybrid retrieval: Combining the precision of traditional search with the understanding of semantic matching
LLM-powered query processing: Using language models to enrich and transform queries
Month 3: You'll be contributing to architectural decisions based on what you've learned building and measuring real implementations.
What You'll Gain.
- Production experience with vector databases and semantic search architectures
- Hands-on work with embedding models for domain-specific use cases
- Understanding of hybrid retrieval – when to use traditional search vs. semantic vs. both
- Fluency in AI-augmented development – not just using Copilot, but integrating AI tools deeply into how you architect, debug, and ship
- The hardest currency in this field: knowing what actually works in production vs. what's just conference talk
What You'll Need:
- Database Foundation (Required)
- 3–5 years of backend engineering experience with databases at the core of your work
- Deep experience with at least one: PostgreSQL, Elasticsearch, MongoDB, or similar production systems
- Solid Python skills – you'll be writing pipelines including pre- and post-processing of queries and results
- Production fundamentals: You understand indexing strategies, query optimization, and why "it works on my machine" doesn't count
- The Right Mindset (Required)
- A willingness to learn new ways of exercising your craft: If you are still arguing with yourself whether AI will ever be able to contribute to your human skills, this is not for you.
- You believe semantic search is where your field is heading – you don't need convincing
- You're frustrated that you haven't had the opportunity to work on this professionally
- You want to make this your specialization, not just add it to a list of things you've tried
- You're ready to rethink how you write software – AI isn't just something you build, it's something that changes how you build
- What We Explicitly Don't Require
- Prior production-level experience with vector databases or embedding models
- AI/ML background or coursework
- Elaborate side projects in semantic search
If you have these, great. If you don't but you have the engineering foundation and the drive to learn – that's who this role is for.
Why This Matters for Your Career.
We're not looking for ML researchers. We're looking for database engineers who see this transition coming and want to be ahead of it. You bring engineering excellence; we provide the projects and mentorship.
What You Can Expect:
- Structured learning path from day one
- Mentorship from engineers who've made this exact transition
- Real production features, not sandbox experiments
- A growing team of 100+ people already executing on AI-enabled strategy – you won't be figuring this out alone
- Influence technical decisions about search architecture and implementation
- Environment where "let's try it and measure it" beats months of planning
- Work that matters: millions of users depend on what you build
- Competitive compensation
- Fully remote within Europe, flexible hours, minimal bureaucracy
- Budget for conferences, training, and the tools you need
- Annual company sailing week in the Mediterranean + team meetups across the EU
You've built the search systems of the last decade. Ready to build what comes next?
About us
At JobLeads our mission is to enable jobseekers to land a better job faster. On this mission we are digitizing every aspect of career coaching to make it accessible and affordable to every jobseeker worldwide.
We are active in 40+ countries and our products reach over 4 million users monthly. We are a fully remote company with a positive, diverse, and supportive culture.
We look for people who are curious, inventive, analytical and work to be a little better every single day. We aim to be smart, humble, hardworking and, above all, collaborative.
