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.

You must be eligible to work in the US without Visa Sponsorship

What You'll 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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