ashby
Senior Machine Learning Engineer: Search Quality
Constructor
Remote, GBR
Full-time
Senior · 4+ years experience
Remote
USD 80000-120000 yearly / year
Discovered 2 weeks ago
LLMPythonPyTorchSQLSparkAirflow
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Role Overview
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.
Key Skills for This Role
LLMPythonPyTorchSQLSparkAirflow
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About us
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.
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