Senior Machine Learning Engineer: Search Quality
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About the role
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. 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.
Requirements
- 4+ years shipping production ML systems
- Experience with search, information retrieval, or recommendation systems
- Hands-on experience with fine-tuning, evaluation frameworks, and scaling LLM deployments
- Strong Python and PyTorch. Fluency in SQL and data orchestration tools (Spark, Airflow)
- Experience designing and running A/B tests to validate model impact
- Excellent English communication skills
- Experience collaborating in cross-functional teams (ranking, product, data engineering)
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