Senior ML Engineer, Legal LLMs — Remote/Europe

ZAAN; Genf; Hybride; CHF 120 000 - 180 000; Plein temps

Résumé du poste

ZAAN in Geneva (remote Europe) seeks a Senior Machine Learning Engineer to own the models behind ZAAN Contract Review, reading contracts against playbooks, ranking issues by severity and proposing redlines a partner would sign off.

You will move models from evaluation to production, fine-tune LLMs, build retrieval over playbooks, and design rigorous evaluation with lawyers in the loop while ensuring data never leaves regional boundaries.

Qualifications

• 5+ years building ML systems that run in production.
• Strong Python and PyTorch; experience with at least one serving stack (vLLM, TensorRT-LLM, TGI or similar).
• Rigour about evaluation: you design test sets, measure error by category and can explain why a model fails.
• Clear written English. Legal, financial or other high-stakes domain experience is a strong plus.

Responsabilités

• Fine-tune, distil and serve large language models for clause classification, risk scoring and redline generation across 38 languages.
• Build retrieval over playbooks and precedent so every flag cites the clause and the deal it came from.
• Design offline and online evaluation with lawyers in the loop: precision/recall per clause type, citation faithfulness, regression gates in CI.
• Cut latency and cost per document without losing accuracy: batching, quantisation, speculative decoding, caching.
• Work with our security team so customer data never trains shared models and stays in the region the customer chose.

Connaissances

Production ML systems

Python

PyTorch

Model serving

Evaluation design

English writing

Legal domain experience

Outils

vLLM

TensorRT-LLM

TGI

Description du poste

ZAAN in Geneva (remote Europe) seeks a Senior Machine Learning Engineer to own the models behind ZAAN Contract Review, reading contracts against playbooks, ranking issues by severity and proposing redlines a partner would sign off.

You will move models from evaluation to production, fine-tune LLMs, build retrieval over playbooks, and design rigorous evaluation with lawyers in the loop while ensuring data never leaves regional boundaries.