At Omnilex, we’re on a mission to transform the way lawyers work. Our AI-native platform lets legal professionals enhance their productivity in legal research and automate workflows. We collaborate closely with our clients and iterate at a market-leading pace. In a year, we have gone from an early MVP to a product used daily by thousands of legal professionals at our clients in Switzerland, Germany and Liechtenstein - and are now scaling rapidly across Europe.
We already stand out with handling unique challenges, including our combination of external data, customer-internal data and our own innovative AI-first legal commentaries.
You’ll be joining a young, passionate, and dynamic team of 15, with roots at ETH Zurich.
Do you love making search actually work well for the user? Are you hands-on with ranking algorithms, query understanding, and excited to ship improvements that users feel the same day? Do you enjoy building pragmatic, low-latency, cost-aware solutions for AI-assisted legal research (where citations, precision, and traceability matter)? If so, we’d love to hear from you.
As an AI Engineer – Legal Search Optimization, you will focus on building and shipping retrieval, reasoning, and context engineering that powers our legal research experience.
Retrieval & ranking: Implement and iterate domain-specific retrieval and reranking algorithms going beyond the standard ones, including knowledge graphs and custom workflows
LLM-powered products: Design and build robust, production-grade LLM systems and chatbots
Signals & features: Design scoring features from citations, authority, recency, jurisdiction, section/paragraph structure, and intra-doc anchors
Practical considerations: Carefully evaluate decisions like API vs. self-hosted; add batching, early-exit, and caching to control cost/latency
Evaluation that guides shipping: Define offline eval sets, run quick ablations, and watch production feedback and dashboards
Search infrastructure: Tune indices, analyzers, and embeddings; manage recall/precision trade-offs and de-duplication/near-duplicate suppression
Cost & performance: Keep token usage, GPU/CPU time, and indexing costs under control with caching, pre-computation, and fallbacks
Collaboration: Work closely with legal experts to turn user pain points into ranking features; document decisions and share clear playbooks
Strong hands-on experience improving search/retrieval systems (hybrid retrieval, reranking, or query understanding) in production
Proven experience in building and deploying LLM-based products from prototyping to production
Solid algorithms background (data structures, complexity, graph theory, statistics), IR/NLP intuition, and practical SQL skills
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 modern embedding models and cross-encoders for reranking; ability to reason about latency, throughput, and quality trade-offs
Ownership mindset, clear communication, and bias for action
Proficiency in English
Availability full-time. On-site in Zurich at least two days per week (hybrid)
You have a Swiss work permit or EU/EFTA citizenship
Working proficiency in German (many sources are in German and we talk to German-speaking customers)
Experience with evaluation pipelines (AI as judge, human-in-the-loop labeling, inter-annotator agreement, error analysis) applied pragmatically
Practical knowledge of sparse methods (BM25+/BM25L/SPLADE), dense models (e5/BGE/ColBERT-style), and semantic re-ranking
Experience deploying/operating small models or services (Docker; basic Kubernetes or serverless is a plus)
Familiarity with our stack: Azure / NestJS / Next.js
Knowledge and experience with legal systems, in particular Switzerland, Germany, USA 🧑⚖️
Direct impact: your ranking and retrieval changes immediately improve result quality and user trust
Autonomy & ownership: Shape our legal research pipeline, across multi-faceted user intention understanding, dynamic retrieval and reranking
Team: Work with a sharp, interdisciplinary team at the intersection of AI, search, and law.
Compensation: CHF 8’000–12’000 per month + ESOP (employee stock options), depending on experience and skills
We’re excited to hear from candidates who are passionate about making legal search fast, accurate, and trustworthy.
omnilex