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# Senior Machine Learning Engineer: Search Quality

- **Company:** Constructors, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $80,000.0 - $120,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Airflow, Information Engineering, Information Retrieval, Python (Programming Language), Machine Learning, Performance Tuning, Recommender Systems, SQL Databases, Pytorch, Large Language Models, Apache Spark, Low Latency, Machine Learning Operations, Data Pipelines
- **Published:** September 25, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2c3410ea08525603

## About the Role

* 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)

## Description

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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