Applied Algorithms Engineer - Information Retrieval at Omnilex

Zurich, Zurich, Switzerland -

Full Time

Start Date

Immediate

Expiry Date

06 Jun, 26

Salary

13000.0

Posted On

08 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Information Retrieval, Algorithms, Optimization, Scoring Functions, Retrieval Pipelines, Rerankers, Query Understanding, LLM Workflows, RAG, TypeScript/Node.js, SQL, Azure AI Search, Pgvector/PostgreSQL, OpenSearch/Elasticsearch, Embedding Models, Latency Engineering

Industry

Software Development

Description

🌟 About You You like problems with a clear objective, messy real-world constraints, and lots of room for cleverness. If you’ve done competitive programming / optimization competitions, you’ll feel at home here: legal search is basically an optimization game where you trade off quality (F2/NDCG), latency (p95), and cost under strict correctness constraints (citations, traceability, jurisdiction). You’ll build scoring functions, retrieval pipelines, rerankers, and evaluation harnesses; and you’ll ship improvements that users notice immediately. You enjoy: Turning vague user intent into formal signals + algorithms Designing fast, low-latency systems under tight budgets Running ablations, debugging failure cases, and iterating quickly Owning the full loop: idea → benchmark → ship → measure 🚀 About Omnilex 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. 🧠 What You’ll Work On As an Applied Algorithms Engineer - Information Retrieval you’ll build the retrieval + ranking + reasoning backbone of our legal research experience. Tasks 🛠 Responsibilities 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 Requirements ✅ Minimum qualifications 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). 🎯 Preferred qualifications (nice-to-have) 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). 🧩 Competitive programming folks: what maps directly You’ll constantly do “contest-style” thinking: 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 🤝 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. Compensation: CHF 8’000–13’000/month + ESOP, depending on experience and skills. If you want to apply your algorithmic instincts to something that matters, and ship improvements that lawyers feel the same day, press Apply.

Responsibilities

The engineer will build the retrieval, ranking, and reasoning backbone for legal research, focusing on tasks like hybrid retrieval, custom reranking, and developing domain-specific workflows involving knowledge graphs and citation awareness. Responsibilities also include designing ranking signals based on citations, authority, and recency, and engineering for low p95 latency.