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# Senior Backend Engineer - Search & Discovery (all genders)

- **Company:** About You
- **Location:** Berlin, Germany
- **Experience:** Expert
- **Salary:** €70,000.0 - €85,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, BigQuery, Data Deduplication, Elasticsearch, Node.Js, Search Technologies, Data Streaming, TypeScript, Datadog, Large Language Models, Backend, Kotlin, Low Latency, Search Engines
- **Published:** September 27, 2026
- **Apply:** https://www.careerjet.de/jobad/de81e6321468597672d204538d492bca34

## About the Role

* You love investigating the "why" behind search outcomes and turning those findings into actionable technical improvements
 * 5+ years of experience building scalable, low-latency backend APIs (Node.js/TypeScript preferred, or fast-converting from Java, Go, or Kotlin)
 * Real depth in Elasticsearch/OpenSearch (mappings, custom analyzers, percolators, scoring functions) beyond just basic query clients
 * Strong algorithmic intuition for ranked data (merging, deduplication, score blending, and latency trade-offs)
 * You're excited to bridge backend engineering and ML by serving model scores, embeddings, or LLM features in production
 * Fluent English communication skills and a pragmatic, data-curious mindset

Nice to Have

 * IR/NLP fundamentals (tokenization, multilingual search, relevance evaluation frameworks)
 * Vector search experience (k-NN/HNSW indices, SigLIP embeddings, recall vs. latency tuning)
 * LLM engineering (prompt caching, timeout budgets, Datadog LLM Observability)
 * Familiarity with BigQuery, dbt, or Dagster pipelines

## Description

* Develop the search engine behind ABOUT YOU, covering query understanding, entity resolution, and semantic vector search in Node.js/TypeScript
 * Blend OpenSearch relevance, embedding similarity, and behavioral re-ranking signals into unified search results
 * Build search-index pipelines (entities, synonyms, k-NN product vectors) streaming data from BigQuery into OpenSearch
 * Integrate LLMs and embedding models (Gemini, Vertex AI) under tight latency budgets with caching and fallback layers
 * Guard search relevance using golden lists, regression suites, and continuous A/B experiments
 * Shape architecture alongside our Tech Lead and collaborate closely with data analysts, engineers, and product managers


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