🌟 About You

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.

🚀 About Omnilex

Omnilex is a young dynamic AI legal tech startup with its roots at ETH Zurich. Our passionate interdisciplinary team of 10+ people is dedicated to empowering legal professionals in law firms and legal teams by leveraging the power of AI for legal research and answering complex legal questions. 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.

🛠️ Your Responsibilities

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-sepcific 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.

📌 Qualifications

✅ Minimum qualifications

• 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 prototypingto 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).