Developer Relations Engineer - EMEA

Posted 2 Days Ago
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Hiring Remotely in London, Greater London, England, GBR
In-Office or Remote
Mid level
Artificial Intelligence • Machine Learning • Software • Database
The Role
As a Developer Relations Engineer, you'll empower developers to build AI applications using Qdrant through demos, content creation, and community engagement while representing Qdrant at events.
Summary Generated by Built In

Qdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations.

Trusted by global leaders like Canva, HubSpot, Tripadvisor, Bosch, and Deutsche Telekom, we’re building the retrieval infrastructure layer for modern AI. Recently raising $50M in Series B funding, we are growing rapidly and committed to transforming how AI understands and interacts with data.

As a remote-first company, we believe diverse backgrounds, perspectives, and experiences fuel innovation. Here, you’ll own meaningful work, tackle challenges, and grow alongside passionate individuals dedicated to shaping the future of AI.

As a Developer Relations Engineer, you will be the face and voice of Qdrant in the heart of the world’s most active AI ecosystem.

You are part engineer, part educator, and part community builder.

Your mission is to empower developers to build sophisticated AI applications using Qdrant through high-quality content, world-class demos, and authentic community engagement.

This is an individual contributor role with ~60% hands-on technical work and ~40% community, content, and speaking.

What you will own
  • Master the Modern AI Stack: Move beyond being a "user" of AI to becoming an expert in the underlying infrastructure (Vector Search, Rust-based performance, and RAG architecture).

  • Build an Elite Network: Represent Qdrant at the highest levels of the SF tech scene, collaborating with founders and engineers from LlamaIndex, CrewAI, Google DeepMind, Mistral AI, Neo4j, and more

  • Establish Thought Leadership: We provide the stage. You will be supported in speaking at premier conferences and publishing content that reaches hundreds of thousands of developers globally.

  • Shape the Product: Act as the direct link between the developer community and our core engineering team, directly influencing our roadmap and feature set.

Responsibilities
  • Building Demos: Develop and maintain open-source "showcase" applications. You’ll build end-to-end RAG pipelines, recommendation engines, and innovative AI search tools that demonstrate Qdrant's power.

  • Applied Innovation: Conduct research into emerging AI patterns (e.g., Long-context RAG, Agentic workflows) to prototype new Qdrant use cases. You will experiment with cutting-edge embedding models and indexing techniques to define the next "killer app" for vector search.

  • Content Creation: Produce educational resources that make complex concepts accessible. This includes writing deep-dive technical blog posts, recording video tutorials, and crafting clear, useful tutorials.

  • Technical Events: Lead Qdrant’s presence at hackathons and workshops, from organizing our own challenges to mentoring developers in the middle of the night as they build their MVPs.

  • Public Speaking: Deliver compelling presentations and live coding sessions at conferences and workshops that inspire developers to rethink what's possible with vector search.

  • Community & Social Engagement: Actively participate in the AI developer community on platforms like Discord, X (Twitter), LinkedIn, and GitHub. Be the "first responder" to technical questions and a champion for developer needs internally.

  • Local Leadership: Host and participate in local and regional meetups, technical mixers, and industry conferences. You will be the primary contact for the local AI scene.

Who you are
  • Technical Depth: You have a strong coding background in Python, Rust, Typescript, or Go and a genuine fascination with vector search, embeddings, and LLMs.

  • Communication Mastery: You can explain the "how" and the "why" of technical architecture with clarity and enthusiasm, whether in writing or on stage.

  • Local Presence: You are already plugged into (or eager to lead) the local tech community. You thrive in the high-energy environment of the AI engineering scene.

  • Creative Mindset: You don't just follow tutorials; you create them. You enjoy finding creative ways to show off technical features through code and storytelling.

  • Autonomy & Empathy: You are a self-starter who can work independently while maintaining deep empathy for the frustrations and successes of the developers you support.

Why join us
  • A remote-first, international team working on cutting-edge AI infrastructure.

  • A competitive salary with additional perks.

  • Flexible working hours and async-friendly culture.

  • High ownership and real impact.

  • Open-source, engineering-driven culture.

  • Choose your own laptop equipment.

For US-based candidates, we also offer a comprehensive benefits package including 401k match, health, dental, and vision insurance, plus flexible PTO policy.


Qdrant is an equal-opportunity employer. We believe the best ideas come from diverse teams, and we actively welcome applicants from all backgrounds. If this role excites you but you don't check every single box, we'd still love to hear from you! We don't want to miss out on great people because of a checklist.

Come build with us!
For information on how we handle your personal data, please refer to our Recruitment Privacy Policy

Skills Required

  • Strong coding background in Python, Rust, Typescript, or Go
  • Experience in community engagement and technical presentations
  • Ability to develop educational resources and technical content
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The Company
0 Employees

What We Do

Qdrant is an open-source vector database and search engine designed for AI applications. It enables fast, scalable similarity search for unstructured data, supporting use cases like semantic search, recommendation engines, and RAG systems. Built in Rust, it provides high-performance retrieval with metadata filtering and real-time updates, serving as a critical infrastructure component for developers building AI-powered platforms.

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