Senior Software Engineer - Search F/H

Qobuz; Pantin; Sur place; EUR 90 000 - 140 000; Plein temps

Résumé du poste

Qobuz is seeking a Lead Search Engineer to own the migration of its search infrastructure to a scalable platform that meets long-term capacity, performance, and feature goals. You’ll be the technical authority on search quality, designing schemas, ranking logic, and query pipelines serving millions of music lovers worldwide.

You will lead architecture migration, own end-to-end relevance, and build analytics and KPI systems while operating a 24/7 production service with strict latency.

Qualifications

• Experience building and operating large production search systems.
• Strong expertise in full-text and semantic search on large-scale catalogs (100M+ records).
• Hands-on experience with modern search stacks (Elasticsearch, OpenSearch, Vespa).
• Experience building ranking, indexing, analytics, and A/B testing pipelines for search.
• Experience defining and tracking KPIs for search quality.
• Experience running 24/7 services with strict latency constraints.

Responsabilités

• Lead design and migration of search architecture to a scalable platform.
• Own search relevance end-to-end: ranking, indexing, query understanding, evaluation.
• Build and maintain analytics, A/B testing, and KPI frameworks for search quality.
• Operate a 24/7 production service handling tens of millions of queries per day with low latency.
• Collaborate with product, data, and catalog teams across data pipeline to query serving.

Connaissances

Full-text search

Semantic search

Elasticsearch

OpenSearch

Vespa

AWS

Data pipelines

ETL

A/B testing

Search analytics

24/7 production service

Outils

PHP (Symfony)

SQL databases

Snowflake

JIRA

Confluence

Description du poste

We’re hiring a Lead Search Engineer to own the migration of our search infrastructure to a new platform that will meet our long-term objectives in capacity, performance, and features. You’ll become the long-term technical authority on search quality at Qobuz — designing schemas, ranking logic, and query pipelines that serve millions of music lovers around the world.

What you’ll do:

• Lead the design and migration of our search architecture to a modern, scalable platform
• Own search relevance end-to-end: ranking, indexing, query understanding, and evaluation
• Build and maintain the analytics, A/B testing, and KPI frameworks that drive continuous improvement in search quality
• Operate a 24/7 production service handling tens of millions of queries per day under strict latency constraints
• Collaborate closely with product, data, and catalog teams in a small, autonomous environment where you own the full stack from data pipeline to query serving.

What we’re looking for:

Proven experience building and operating a large production search system
Strong expertise in full-text and semantic search on large-scale catalogs (100M+ records) across multiple languages
Hands‑on experience with modern search stacks such as Elasticsearch, OpenSearch, or Vespa
Experience building ranking, indexing, analytics, and A/B testing pipelines for search
Experience defining and tracking success and failure KPIs for search quality
Track record running a 24/7 service with strong operational constraints — tens of millions of queries per day, tight response‑time SLAs
Solid grounding in information retrieval fundamentals: BM25, text matching, scoring mechanics, relevance tuning
Experience with AWS cloud architecture
Comfort with data pipelines and ETL (batch and streaming), working with cloud data warehouses and object storage
Experience leading at least one major system migration — dual‑write patterns, shadow testing, gradual rollouts, and rollback strategies

Our stack:

• API: PHP (Symfony) — PHP/Symfony experience is a plus
• Data: SQL databases, Snowflake data warehouse
• Cloud: AWS
• Tooling: JIRA, Confluence

Bonus points:

• Experience with metadata quality issues in the music industry
• Experience with personalization algorithms
• Using ML tools to improve metadata quality
• Familiarity with ML‑based ranking techniques (learning to rank, embedding‑based retrieval)
• Knowledge of relevance evaluation metrics (NDCG, MRR, MAP)
• Experience with multilingual and CJK search challenges (tokenization, multi‑script handling)
• Experience working in a small, autonomous team owning the full stack from data pipeline to query serving