Backend Engineer

• Build search-index pipelines (entities, synonyms, k-NN product vectors) streaming data from BigQuery into OpenSearch
• Integrate LLMs and embedding models (Gemini, Vertex AI) under tight latency budgets with caching and fallback layers
• Guard search relevance using golden lists, regression suites, and continuous A/B experiments
• Shape architecture alongside our Tech Lead and collaborate closely with data analysts, engineers, and product managers

• 5+ years of experience building scalable, low-latency backend APIs (Node.js/TypeScript preferred, or fast-converting from Java, Go, or Kotlin)
• Real depth in Elasticsearch/OpenSearch (mappings, custom analyzers, percolators, scoring functions) beyond just basic query clients
• Strong algorithmic intuition for ranked data (merging, deduplication, score blending, and latency trade-offs)
• You’re excited to bridge backend engineering and ML by serving model scores, embeddings, or LLM features in production
• Fluent English communication skills and a pragmatic, data-curious mindset

Nice to Have

• IR/NLP fundamentals (tokenization, multilingual search, relevance evaluation frameworks)
• Vector search experience (k-NN/HNSW indices, SigLIP embeddings, recall vs. latency tuning)
• LLM engineering (prompt caching, timeout budgets, Datadog LLM Observability)
• Familiarity with BigQuery, dbt, or Dagster pipelines

Different backgrounds and perspectives make us stronger and shape our culture in ways that matter.