Applied Algorithms Engineer - Information Retrieval

• Retrieval & ranking beyond the defaults
• Hybrid retrieval (sparse + dense), custom reranking, multi-stage pipelines
• Domain-specific workflows (e.g., knowledge graphs, citation-aware expansions, jurisdiction filters)
• Scoring & features (where algorithms meet relevance)
• Build ranking signals from: citations, authority, recency, jurisdiction, document structure, paragraph/section anchors
• Combine signals into robust scoring functions and reranking strategies
• Query understanding & intent routing
• Classify query intent, detect constraints ("Swiss law", "latest", "doctrine vs. case law"), rewrite/expand queries
• Route to the right retrieval strategy with minimal overhead
• Evaluation that actually guides shipping
• Build offline eval sets, define metrics, run quick ablations
• Use production feedback + dashboards to close the loop (what improved? what broke?)
• Search infrastructure + performance engineering
• Tune indices/analyzers/embeddings, manage recall vs. precision, deduplicate near-duplicates
• Engineer for p95 latency: caching, batching, early-exit strategies, fallbacks
• LLM-powered product systems
• Design and ship production-grade LLM workflows (RAG, tool use, citation-grounded answers)
• Keep outputs traceable, verifiable, and safe for legal professionals
• Collaboration with domain experts
• Work closely with legal experts to translate pain points into ranking logic
• Document decisions and build playbooks others can extend, * define objective * pick strategy * optimize bottlenecks * prove it with measurements
• The difference is: the test cases are real users, and the constraints include cost + latency + trust + citations.

Benefits

• Direct impact: your ranking and retrieval changes immediately improve user trust and result quality.
• Autonomy & ownership: shape the core search pipeline end-to-end (intent * retrieval * reranking * grounded answers).
• Team: sharp, interdisciplinary people at the intersection of AI, search, and law.

You like problems with a clear objective, messy real-world constraints, and lots of room for cleverness., * Strong hands-on experience improving search / retrieval systems in production (hybrid retrieval, reranking, query understanding).

• Proven experience building and deploying LLM-based products from prototype to production.
• Strong algorithms background (data structures, complexity, graphs, probability/statistics) and practical SQL.
• Proficiency in TypeScript/Node.js (our core stack).
• Experience with one or more of: Azure AI Search, pgvector/PostgreSQL, OpenSearch/Elasticsearch, or similar.
• Familiarity with embedding models + cross-encoders, and the ability to reason about latency/throughput/quality trade-offs.
• Ownership mindset, clear communication, bias for action.
• Proficiency in English.
• Full-time availability. Zurich-based with on-site presence at least 2 days/week (hybrid)., * Swiss work permit or EU/EFTA citizenship.
• Working proficiency in German.
• Experience with evaluation pipelines (human labeling, inter-annotator agreement, error analysis, AI-as-judge-used pragmatically).
• Knowledge of sparse/dense IR methods (BM25 variants, SPLADE, e5/BGE, ColBERT-style) and semantic reranking.
• Experience operating services (Docker; basic Kubernetes/serverless is a plus).
• Familiarity with Azure / NestJS / Next.js.
• Exposure to legal systems (especially Switzerland, Germany, USA).

Omnilex is a young, dynamic AI legal tech startup with roots at ETH Zurich. Our interdisciplinary team (14+ people) empowers legal professionals by building AI systems for legal research and answering complex legal questions; across external sources, customer-internal documents, and our own AI-first legal commentaries.