Search Architect
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Job Location:
Birmingham, MI - USA
Monthly Salary:
Not Disclosed
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
Were building intelligent product search that understands intent learns from behavior and gets smarter over time. As our ML Architect for Search youll design the retrieval and ranking systems that power product discovery for millions of usersbalancing cutting-edge ML with real-time performance constraints.
This is modern ML-first search architecture: embedding models vector similarity cross-encoder reranking and multi-model orchestration under strict latency budgets. Your work directly impacts conversion revenue and customer experience.
You must be eligible to work in the US without Visa Sponsorship
What Youll Do
Design hybrid retrieval systems combining keyword search vector similarity and cross-encoder reranking at scale.
Build intelligent query routing with cascading classification strategies
Architect multi-model inference pipelines optimized for latency-sensitive workloads
Define relevance metrics run A/B experiments and drive measurable business outcomes
Support the driving MLOps standards for model deployment monitoring and continuous improvement
Partner with Product Merchandising and Engineering to translate business requirements into ML solutions
Mentor engineers and define search and ML architectural standards
What You Bring
Required
7 years in software data or ML engineering with 3 years building production search systems
Experience with e-commerce search patterns: faceting merchandising rules query understanding
Strong knowledge of embedding models approximate nearest neighbor search and reranking architectures
Hands-on experience with vector databases and similarity search at scale (Pinecone Milvus Weaviate FAISS or similar)
MLOps expertise: model deployment pipelines monitoring versioning and retraining workflows
Production experience with transformer-based models for classification and ranking
Track record balancing latency cost and relevance tradeoffs in real-time systems
Experience designing controlled experiments and defining ML success metrics
Nice to Have
Experience with enterprise search platforms (Algolia OpenSearch Elastic or similar)
Background in Learning-to-Rank and multi-stage retrieval architectures
Cloud ML platform experience (AWS SageMaker GCP Vertex AI or Azure ML)
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Required Experience:
Staff IC
