Senior Support Engineer
Job Description
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About Qdrant
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.
What You'll Own
Provide direct, Slack-first technical support to customers running Qdrant, mostly asynchronous rather than face-to-face.
Troubleshoot production issues across Kubernetes, cloud infrastructure, networking, storage, and the database layer.
Lead incident investigations and turn recurring problems into documented diagnostics and repeatable fixes, using AI-assisted tooling to speed up triage where it helps.
Partner with engineering and platform teams to resolve customer issues and feed learnings back into the product.
Build and maintain internal tools that make support workflows faster and more observable.
Write and maintain clear documentation for both customers and the internal team.
Lead by example through strong technical work, initiative, and collaboration with stakeholders across the company.
Who You Are
At least 5+ years in a customer-facing support or infrastructure role, ideally supporting production Kubernetes environments.
Strong, hands-on Kubernetes skills. A Certified Kubernetes Administrator (CKA) credential is a plus.
Proficient in Python or a similar language for scripting and automation.
Experience with Kafka or similar distributed messaging systems.
Comfortable working in AWS, GCP, or Azure.
Solid understanding of database internals and how they behave under production load.
A proactive, ownership-driven mindset. You dig into ambiguous problems without waiting to be asked.
Clear communicator who can translate between technical and non-technical audiences.
Nice to Have
Familiarity with vector search or vector database technologies.
Experience with Terraform and infrastructure-as-code. A security background is a plus.
Experience with observability tools (e.g., Prometheus or Grafana) and automating support workflows.
Why Join Us
A remote-first, international team working on cutting-edge AI infrastructure.
A competitive salary with additional perks.
Flexible working hours and an async-friendly culture.
High ownership and real impact.
Open-source, engineering-driven culture.
Choose your own laptop equipment.
For US-based full-time employees, we also offer a comprehensive benefits package including 401k match, health, dental, and vision insurance, plus a flexible PTO policy.
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Qdrant
View Company ProfileQdrant (operating under qdrant.tech, legally Qdrant Berlin GmbH) is the premier, enterprise-grade open-source vector database management system, high-performance neural search engine, and similarity matching orchestration powerhouse engineered to act as the definitive, high-velocity infrastructure layer for Retrieval-Augmented Generation (RAG), large language model (LLM) persistent memory, and multimodal AI applications. Founded in 2021 by expert distributed systems and machine learning technologists Andre Zayarni and Andrey Vasnetsov, the corporate ecosystem completely eliminates the severe performance degradation, index-rebuilding latency spikes, and structural memory erosion commonly associated with legacy relational database plug-ins and primitive K-Nearest Neighbor (KNN) wrappers by building its custom storage architecture entirely from scratch in Rust. Moving far beyond traditional keyword search methods or high-latency post-filtering mechanisms, Qdrant natively unifies expansive JSON metadata payload filtering during HNSW graph traversal, native hybrid search combining dense and sparse vectors, ColBERT-based token-level late interaction reranking, and dynamic multivector storage configurations into a single high-availability data infrastructure framework. As the primary engine trusted by global developer communities and high-scale enterprise platforms, its open-source repository commands over 29,000 GitHub stars and powers critical AI pipelines for forward-thinking organizations globally. Backed by elite international venture groups, the enterprise has secured over $87 million in institutional funding—culminating in a massive Series B capital acceleration led by Spark Capital alongside 42 Capital and AVP to scale its managed Qdrant Cloud operations and native inference engine integrations. Under the hood, its technology stack harnesses advanced vector quantization strategies (including scalar and binary quantization) to execute up to a 64x memory footprint reduction with minimal recall loss, alongside real-time data indexing capabilities that expose newly added vector points instantaneously to active queries. Headquartered in Berlin, Germany, with a globally distributed remote engineering presence, the firm operates with absolute deterministic execution correctness across multi-tenant cloud and on-premise infrastructure environments. What sets Qdrant apart is its uncompromising dedication to replacing complex, multi-stage data orchestration loops with real-time, one-stage filtered similarity traversal and deep memory optimization; by bridging the gap between high-volume programmatic data repositories and millisecond-level neural vector retrieval, the corporation remains a definitive cornerstone of modern infrastructure scaling and worldwide applied artificial intelligence transformation.
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