Engineering Internship - AI​/Data

Job overview

Title:

Engineering Internship – AI/Data

Department:
Engineering

Location:

Zurich – on‑site at least two days per week (hybrid)

Compensation: CHF 2500–4500 per month, depending on experience and skills.

• Build and improve ingestion and processing pipelines (APIs, bulk imports, parsing, normalization).
• Support chunking, metadata extraction, citation handling, labeling/tagging, and schema design so downstream AI works reliably.
• Assist with indexing strategies, embeddings, hybrid retrieval, reranking experiments, and relevance tuning.
• Build small evaluation sets, run experiments, analyze failure cases, and help turn findings into shipped improvements.
• Prototype and implement pragmatic LLM workflows (summarization, classification, extraction, RAG components) with attention to cost/latency.
• Write clean Type Script code, add tests where appropriate, and improve observability (basic metrics/logging) for pipelines/services.
• Collaborate closely with engineers and domain experts; document decisions and share learnings clearly.

• Near completion of a degree (e.g., finished Masters program or close to graduating) in Computer Science, Data Science, or a related field (in progress) – or equivalent practical experience.
• Solid programming skills (preferably Type Script/Node.js).
• Comfortable with data structures, algorithms, and working with real‑world data (edge cases, messy inputs).
• Working knowledge of SQL (ideally Postgre
  
  SQL).
• Interest in applied AI/ML systems (LLMs, embeddings, retrieval, evaluation) and willingness to learn fast.
• Strong ownership mindset and clear communication in English.
• Availability full‑time for the internship, ideally 6 months.

• Type Script / Node.js
• PostgreSQL
• AIOpenSearch, Azure, Elastic Search
• Cloud, CI/CD, Docker
• LLM

• Direct impact:
  Your work ships into the product and improves real user outcomes.
• Learning & mentorship:
  Hands‑on exposure to production AI/search/data systems in a high‑ownership environment.
• Breadth:
  Work across ingestion, indexing, retrieval, LLM workflows, and evaluation.
• Team:
  Join an interdisciplinary group at the intersection of AI, engineering, and law.