Constructor

Senior Machine Learning Engineer: Search Quality (Remote)

🇪🇸 Remote, ES Remote IT Senior Posted May 7, 2026
LocationRemote, ES
WorkplaceRemote
SenioritySenior
CategoryIT
IT CategoryData Science & ML
LanguageEnglish
PostedMay 7, 2026
Last verifiedMay 9, 2026

Launched in 2019, Constructor is an AI-first ecommerce search and discovery platform that helps shoppers find the right products at the right time and enables leading global e-commerce brands to drive meaningful revenue and conversion gains.

As a Senior Machine Learning Engineer in the Search Quality team, you will improve the e-commerce experience for hundreds of millions of users across the world by building the systems that power relevance for global retailers - from fashion and grocery to electronics and hardware.

The mission is to measure search quality, push it higher, and catch degradations before the user does. You will achieve this through a blend of fine-tuned LLMs for relevance judgment, real-time models, and deep offline analysis of query logs.

About the Job

Your primary focus will be relevance evaluation and quality improvements:

  • LLM-based evaluation. We fine-tune our own models to assess relevance. This involves teaching the model to understand query intent, represent items from messy catalog data, and align model judgments with real user behavior.
  • Real-time quality in production. Reranking, filtering, signal computation. Latency is a strict requirement, so quality vs speed tradeoff is constant.                 
  • Automated quality monitoring and agentic insights. Pipelines to detect degradations and find underperforming patterns. Agent-based systems that generate actionable recommendations for the product data and search configurations.

What makes this interesting

  • Multi-domain, multi-language, at scale - 40+ languages, 20+ domains. The models need to generalize across all of them - without per-customer rules or overrides.
  • No universal ground truth. A grocery retailer and a fashion retailer may have different perceptions on what "relevant" means.
  • Efficiency at scale. Optimizing and scaling LLM inference across our entire customer base.

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