Applied Algorithms Engineer - Information Retrieval

🌟 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.

🛠 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

✅ 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 Type Script/Node.js (our core stack).
• Experience with one or more of:
  Azure AI Search, pgvector/Postgre
  
  SQL, Open Search/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

• 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.