Position Details
About this role
Designs and implements advanced ML-driven search and ranking systems to enhance product discovery, balancing performance and relevance.
Key Responsibilities
- Design retrieval systems
- Build ranking architectures
- Implement embedding models
- Optimize latency
- Support MLOps pipelines
Technical Overview
Focuses on building scalable, low-latency retrieval architectures using embedding models, vector similarity search, and MLOps pipelines, supporting millions of users.
Ideal Candidate
The ideal candidate is a senior ML architect with 7+ years in search systems, specializing in retrieval, ranking, and embedding models. They have extensive experience with vector similarity search, MLOps pipelines, and deploying scalable ML solutions for e-commerce applications.
Must-Have Skills
Nice-to-Have Skills
Tools & Platforms
Required Skills
Hard Skills
Soft Skills
Industry & Role
Keywords for Your Resume
Deal Breakers
Lack of experience with search architecture, No background in ML models or vector similarity, Unwilling to work onsite in Birmingham
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