Machine Learning Search Engineer

We are seeking ML Search Engineers who enjoy building a modern, machine-learning–driven search platform that powers product discovery across e-commerce ecosystem. As an ML Search Engineer, you’ll work hands-on implementing and supporting the machine learning pipelines that drive intelligent search, relevance, and intent understanding.

This role sits at the core of a newly formed Search Engineering team and works closely with a senior ML Architect and the Innovation organization. You’ll help take an existing ML search proof-of-concept and evolve it into a scalable, production-ready system used across dot-com and internal branch platforms.

This is a hands-on engineering role focused on building, operating, and improving ML systems in production—not research-only work.

Responsibilities:

• Implement ML-driven search components designed by the Search Architect
• Build and maintain Python-based ML pipelines for embeddings, inference, and relevance
• Work with vector search and similarity matching to support intent-based product discovery
• Support GPU-based workloads for model computation and inference
• Participate in MLOps workflows, including deployment, monitoring, retraining, and maintenance
• Help rerun and refresh embeddings as product data evolves over time
• Collaborate with Innovation, Architecture, and Engineering teams to produce ML systems
• Debug, optimize, and improve performance, reliability, and relevance of search pipelines
• Contribute to ongoing improvements as the search platform evolves

Required Qualifications:

• Strong Python development experience (primary language)
• Experience building or supporting machine learning pipelines in production
• Understanding of ML lifecycle concepts (training, inference, retraining, monitoring)
• Familiarity with MLOps principles
• Experience working with large datasets and model outputs
• Ability to work hands-on with evolving systems and ambiguous requirements
• Strong problem-solving and collaboration skills

Preferred Qualifications:

• Experience with vector databases or similarity search
• Exposure to embeddings, semantic search, or recommendation systems
• Experience with GPU-based workloads
• Cloud ML experience (GCP, AWS, or Azure)
• Prior work on e-commerce search, discovery, or relevance systems