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$37k – $62k per year
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In office (Zurich)
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Intern
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iudexnc cuts coverage-decision cost by over 80%. Agents extract facts, run a five-dimension policy check and draft audit-ready reasoning.

Position: Engineering Internship - AI/Data

Department: Engineering

Location: Zurich

Status: Open

About You

Youre in the final stretch of your studies (in particular graduated Masters student or close to graduation) and you want a hands-on internship where you build real AI systems that ship to production. You enjoy working with messy data, writing solid code, and running experiments to improve LLM-powered workflows. You learn fast, take ownership, and like collaborating closely with a small team.

About INC

INC (Iudex Non Calculat) is a pre-seed AI startup based in Zurich, building infrastructure that automates coverage checks and case handling for legal insurance companies. Legal insurance is a €16.8B European market where every case still requires 15-45 minutes of manual review. We compress that to under 2 minutes. Were backed by a paid pilot with a Swiss insurer and a founder with 120+ B2B customers at his previous AI legal tech company. We work at the intersection of LLM agent systems, document understanding, and domain-specific reasoning - where accuracy, traceability, and auditability matter.

Benefits

- Direct impact: Your work ships into the product and directly improves how legal insurers process cases.

- Learning & mentorship: Hands-on exposure to production LLM agent systems, search, and data pipelines in a high-ownership environment.

- Breadth: Work across document ingestion → policy parsing → LLM reasoning → evaluation.

- Founder access: Work directly with the founder on architecture, product, and technical decisions.

- Compensation: CHF 2500-4000 per month, depending on experience and skills.

- Prospect of a permanent position: Possibility of full-time employment with equity (ESOP) after a successful internship.

Were excited to hear from candidates who are finishing their studies and want a high-impact engineering internship in AI. Apply today by pressing the Apply button.

Requirements

- Near completion of your degree (e.g. finished Masters program or close to graduating).

- Degree in Computer Science, Data Science, Mathematics, or a related field (in progress) - or equivalent practical experience.

- Solid programming skills (preferably TypeScript/Node.js).

- Comfortable with data structures, algorithms, and working with real-world data (edge cases, messy inputs).

- Working knowledge of SQL (ideally PostgreSQL).

- Interest in applied AI/ML systems (LLMs, embeddings, retrieval, evaluation, agentic workflows) and willingness to learn fast.

- Strong ownership mindset and clear communication in English.

- Availability full-time for the internship, ideally 6 months. On-site in Zurich at least twice per week.

Responsibilities

- Support the founder across data pipelines, LLM agent workflows, and evaluation infrastructure; go deeper in one area while getting exposure to the full stack.

- Data pipelines: build and improve ingestion and processing pipelines for insurance policy documents, legal texts, and case descriptions (parsing, normalization, structuring).

- AI-ready structuring: support chunking, metadata extraction, clause mapping, and schema design for reliable downstream LLM agent operation.

- LLM agent workflows: prototype and implement agentic LLM workflows for coverage analysis - fact extraction, policy cross-referencing, exclusion checking, and decision drafting - with attention to accuracy, cost, and latency.

- Search & retrieval: assist with indexing strategies, embeddings, hybrid retrieval, and relevance tuning for legal and insurance content.

- Evaluation & quality: build eval sets from real coverage check examples, run experiments, analyze failure cases, and help turn findings into shipped improvements.

- Engineering fundamentals: write clean TypeScript code, add tests where appropriate, and improve observability (metrics/logging) for pipelines and services.

- Collaboration: work closely with the founder and domain experts; document decisions and share learnings clearly.

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