Search & Relevance Engineer, Semantic / AI Search (Europe, Remote)

We're hiring a senior Search & Relevance Engineer for a fast-growing, US-based B2B SaaS company.

You'll own one question: when a user describes who they're looking for, do we give them the right results? You'll measure relevance, find what's broken and ship the fix yourself, working with AI coding agents every day. This is a full-time remote role.

What you'll do

• Own search quality across filter search (Elasticsearch) and AI search (natural language to filters).
• Build the measurement: relevance benchmarks, labeled query sets, offline evals and online metrics. Online, that means tracking whether searches end in a saved result, a refined search, or an abandoned search.
• Tune Elasticsearch: mappings, analyzers, keyword and industry matching, scoring, synonyms, and handling of messy company names and domains.
• Build semantic and hybrid search: embed company and person profiles, run vector search alongside keyword search, and tune how the two are blended.
• Use embeddings to classify companies (industry, business type, B2B/B2C), group similar companies, and power companies like this one.
• Improve AI search through prompts, schema, model choice and evals. For example, Series A fintechs in Berlin with 50 to 200 employees should become the right set of filters.
• Find where the data is missing or wrong, measure the impact, and recommend what to fix, enrich or buy.
• Benchmark data coverage, accuracy and freshness against external sources, and turn the results into a roadmap.
• Ship code, not just reports, across a TypeScript backend and Python analysis tooling. Use AI to label data, analyze queries and run evals at a scale one person couldn't manage by hand.

You may be a fit if

• You're AI-native. You build with AI coding agents (Claude Code, Cursor, or similar) every day, run agents for analysis and labeling, and can show how that makes you faster.
• You have a product mindset. You start from what the user was trying to do and why the search failed them, then pick the fix that changes their outcome and explain the trade-off to product and leadership.
• You have 4+ years working on search, ranking or recommendations, with production Elasticsearch or OpenSearch experience (Solr or Vespa also count).
• You have production experience with embeddings. That covers choosing and evaluating models, vector indexes (Elasticsearch dense_vector/kNN, pgvector, Qdrant), hybrid ranking such as reciprocal rank fusion, and managing index size, latency and re-embedding cost at hundreds of millions of rows.
• You've built relevance evaluations (judgment sets, precision@k, nDCG) and changed a product because of what they showed.
• You have strong SQL and Python, and you're comfortable shipping in a TypeScript/Node backend with AI agents.
• You've shipped LLM features: prompting, structured outputs, eval harnesses, and cost and latency trade-offs.
• You're comfortable with messy entity data: company matching, deduplication, classification and industry taxonomies.

Nice to have

• Experience on a marketplace, e-commerce or B2B SaaS search team.
• Entity resolution or learning-to-rank experience.
• You've evaluated or integrated third-party data vendors.

What we offer

• A full-time, fully remote role.
• End-to-end ownership of search quality for a product used at large scale.
• An AI-native team where shipping with agents is the default.
• Direct work with product and leadership.