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Constructor

SaaS search and product discovery platform

Senior Machine Learning Engineer: Search Quality

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeRemote in Germany

About the Job

Requirements

  • At least 4 years of experience shipping production machine-learning systems.
  • Experience with search, information retrieval, or recommendation systems.
  • Hands-on experience with fine-tuning, evaluation frameworks, and scaling large language model deployments.
  • Strong Python and PyTorch skills, plus fluency in SQL and data orchestration tools such as Spark and Airflow.
  • Experience designing and running A/B tests to validate model impact.
  • Excellent English communication skills.
  • Experience collaborating in cross-functional teams, including ranking, product, and data engineering.

Responsibilities

  • Improve search relevance for global retailers by building systems that power relevance.
  • Measure search quality, improve it, and detect degradations before users notice them.
  • Fine-tune models to assess relevance by teaching models to understand query intent, represent items from messy catalog data, and align model judgments with real user behavior.
  • Build real-time quality systems for reranking, filtering, and signal computation while balancing quality and speed under strict latency requirements.
  • Develop automated quality-monitoring pipelines to detect degradations and identify underperforming patterns.
  • Build agent-based systems that generate actionable recommendations for product data and search configurations.
  • Optimize and scale large language model inference across the customer base.

About the Company

Constructor

SaaS search and product discovery platform

Constructor.io provides a SaaS search and product discovery platform for ecommerce. It helps large ecommerce brands personalize online experiences and improve key metrics by using customer clickstream data and transformer-based machine learning models to understand and predict user behavior. The platform is API-first, headless, and composable, so it can plug into a companyโ€™s existing systems and workflows. It is designed to reduce manual merchandising tasks and empower ecommerce teams to act more strategically and quickly. Unlike many competitors, it emphasizes enterprise-grade integration, strong customer success through dedicated managers, training, performance reviews, and live support, and a subscription-based model that ensures ongoing support and updates. The goal is to help clients optimize digital merchandising, personalize customer experiences, and boost business KPIs such as conversion, revenue, and efficiency.

Company size
1,001-5,000
Company stage
Series B
Total funding
$86.1M
Headquarters
San Francisco, CA
Founded
2015

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