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We're building intelligent product search that understands intent, learns from behavior, and gets smarter over time. As our ML Architect for Search, you'll design the retrieval and ranking systems that power product discovery for millions of users—balancing 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.
Job Responsibility:
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
Requirements:
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
Must be eligible to work in the US without Visa Sponsorship
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)
What we offer:
We offer comprehensive benefit plans and programs designed to support your health and wellness, provide income protection and build financial security for your retirement