AI Engineer - Legal Search

Responsibilities

• Retrieval & ranking:
  Implement and iterate domain‑specific retrieval and reranking algorithms that go beyond standard methods, including the use of knowledge graphs and custom workflows.
• LLM‑powered products:
  Design and build robust, production‑grade LLM systems and chatbots that serve legal research needs.
• Signals & features:
  Design scoring features based on citations, authority, recency, jurisdiction, section/paragraph structure, and intra‑document anchors.
• Practical considerations:
  Evaluate trade‑offs such as API vs. self‑hosted options; add batching, early‑exit, and caching to control cost and latency.
• Evaluation:
  Define offline evaluation sets, run quick ablations, and monitor production feedback and dashboards to guide shipping decisions.
• 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 translate 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 building and deploying LLM‑based products from prototype to production.
• Solid algorithms background (data structures, complexity, graph theory, statistics), IR/NLP intuition, and practical SQL skills.
• Proficiency in Type Script/Node.js (our core stack).
• Experience with Azure AI Search, pgvector/Postgre
  
  SQL, Open Search/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.
• Full‑time availability; on‑site in Zurich at least two days per week (hybrid).

• Direct impact:
  Your ranking and retrieval changes immediately improve result quality and user trust.
• Autonomy & ownership:
  Shape the legal research pipeline, from user intention understanding to 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 plus ESOP (employee stock options), depending on experience and skills.

Omnilex is a young, dynamic AI legal‑tech startup rooted at ETH Zurich. Our interdisciplinary team of 14+ people empowers legal professionals in law firms and in‑house legal teams by leveraging AI for legal research and answering complex legal questions. We address unique challenges by combining external data, customer‑internal data, and innovative AI‑first legal commentaries.

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 would love to hear from you.