Search / Relevance Engineer (AI-Native Product)

Search / Relevance Engineer (AI-Native Product)

We’re partnering with a high-growth AI-native startup building a new category of revenue and GTM infrastructure for modern companies. They’re replacing fragmented legacy systems with a unified, intelligent platform used by fast-scaling startups.

They’re now looking for a Search / Relevance Engineer to own and scale the core systems that power retrieval, ranking, and semantic understanding across their product.

This is a foundational role focused on building search that is fast, context-aware, and deeply aligned with user intent across both structured and unstructured data.

What you’ll do

You’ll own and evolve the full search stack - from indexing to ranking to evaluation:

• Design and improve end-to-end search systems (indexing → retrieval → ranking → evaluation)
• Own search relevance and quality metrics (precision, recall, NDCG, and related IR metrics)
• Build and optimise indexing pipelines (tokenisation, normalisation, schema design, data modelling)
• Develop and iterate on ranking strategies:
  • Keyword-based retrieval (BM25, hybrid search)
  • Embedding-based retrieval (dense / vector search)
  • Learning-to-rank and ML-based ranking models
• Work with modern embedding and semantic search systems (e.g. OpenAI embeddings, Sentence Transformers, vector databases)
• Design hybrid retrieval systems combining lexical + semantic approaches
• Build evaluation frameworks:
  • Offline benchmarks and labelled datasets
  • Human relevance judgement pipelines
  • A/B testing and live experimentation
• Analyse search logs to identify failure modes, relevance gaps, and ranking issues
• Partner closely with product and data teams to align search quality with real user intent and product outcomes

What we’re looking for

• Strong experience building production search or relevance systems
• Deep understanding of information retrieval fundamentals (ranking, retrieval, evaluation)
• Experience with hybrid search systems and vector-based retrieval
• Strong backend and data systems capability, with exposure to ML-driven ranking approaches
• Analytical mindset with experience running experiments and iterating on relevance quality

Nice to have

• Experience from search / infra-heavy companies (e.g. Elastic, Redis, MongoDB, similar environments)
• Exposure to LLM-based retrieval or AI-native search systems
• Experience in early-stage or high-ownership environments