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10 дней назад

Senior Backend Engineer - Search & Discovery

70 000 - 85 000€
Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Английский
c1
Страна
Germany
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR
Senior Backend Engineer - Search & Discovery (Node.js/TypeScript, OpenSearch): Building the search engine behind a fashion e-commerce platform with an accent on query understanding, semantic vector search, and low-latency backend services. Focus on blending relevance, embeddings, and behavioral signals, streaming product data into search indices, and integrating LLM features with caching and fallback layers.

Location: Hamburg or Berlin, Germany

Salary: €70,000–€85,000 per year

Company

hirify.global is a European e-commerce company combining fashion and technology to create personalized online shopping experiences.

What you will do

  • Develop the search engine covering query understanding, entity resolution, and semantic vector search using Node.js and TypeScript.
  • Combine OpenSearch relevance, embedding similarity, and behavioral re-ranking signals into unified search results.
  • Build search-index pipelines for entities, synonyms, and k-NN product vectors, streaming data from BigQuery into OpenSearch.
  • Integrate Gemini and Vertex AI models with caching and fallback layers under strict latency budgets.
  • Maintain search relevance through golden lists, regression suites, and continuous A/B experiments.
  • Shape the architecture with the Tech Lead and collaborate with data analysts, engineers, and product managers.

Requirements

  • 5+ years of experience building scalable, low-latency backend APIs.
  • Strong Node.js and TypeScript experience, or the ability to transition quickly from Java, Go, or Kotlin.
  • Deep Elasticsearch or OpenSearch expertise, including mappings, custom analyzers, percolators, and scoring functions.
  • Strong algorithmic understanding of ranked data, including merging, deduplication, score blending, and latency trade-offs.
  • Experience 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, multilingual search, and relevance evaluation frameworks.
  • Vector search experience with k-NN/HNSW indices, SigLIP embeddings, and recall-versus-latency tuning.
  • LLM engineering experience, including prompt caching, timeout budgets, and Datadog LLM Observability.
  • Familiarity with BigQuery, dbt, or Dagster pipelines.

Culture & Benefits

  • Flat hierarchies, direct communication, pragmatic decision-making, and clear ownership.
  • Inclusive environment that values different backgrounds and perspectives.
  • Team lunches, afterwork drinks, company events, and informal opportunities to connect.
  • Additional employee perks are provided through the company benefits program.

Hiring process

  • Apply online through the career page.
  • The hiring team will follow up after reviewing the application.

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