10 дней назад
Senior Backend Engineer - Search & Discovery
70 000 - 85 000€
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
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
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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