Senior Staff Backend Engineer, Faire

• 8+ years of industry experience building and scaling distributed systems, with strong fundamentals in distributed infra
• 5+ years working on search, ranking, or query understanding systems at scale
• Hands-on experience with traditional, semantic, and hybrid search frameworks (e.g. Elasticsearch, Lucene, Solr, FAISS, Vespa, Weaviate, Milvus)
• Expertise in search indexing, retrieval, and ranking pipelines, including query rewriting, personalization, embeddings, and vector search
• Experience with ML-driven search systems, including personalization, re-ranking, and reinforcement learning
• Strong knowledge of latency optimization and building low-latency, high-throughput serving systems
• Familiarity with AI/ML infra such as model serving (e.g. TensorFlow Serving, TorchServe, Ray Serve), feature stores, and online experimentation
• Proficiency in at least one modern backend language (Java, Kotlin, Go, or Python)
• Solid understanding of APIs, distributed data storage, and streaming systems (e.g. Kafka, Flink, Spark)
• Excellent communication skills and a proven track record of technical leadership and mentorship

Desirable

• As a technical leader on the Discovery Infra and Enablement team, you will be responsible for building and scaling the next generation of Search and AI infrastructure at Faire.
• You’ll unlock new agentic features, enable cutting-edge retrieval and ranking applications, and ensure our search experience is world-class in both relevance and latency
• Build and optimize the next-generation Discovery Search and AI stack, integrating LLMs, semantic search, dense vector retrieval, embeddings, and reinforcement learning to serve personalized results with <100ms latency
• Lead the design and productionization of natural language search and intelligent discovery systems, enabling AI agents to generate relevant collections, explain results, and support retailers in browsing, filtering, and evaluation
• Tackle deep technical challenges in query understanding, retrieval efficiency, and multi-stage ranking pipelines
• Develop infrastructure to support state-of-the-art search algorithms while optimizing for cost, speed, and accuracy at scale
• Shape the technical strategy for how applied AI is leveraged across search and discovery at Faire
• Collaborate with Data Scientists, Product Managers, and ML Engineers to translate business needs into scalable technical solutions