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Giskard
Permanent (CDI) Full-time Senior AI quality Hybrid 231 Rue Saint-Honoré, Paris, IDF 75001, France · Hybrid Paris, France EUR 70k–95k/year + BSPCE This listing is optimized for AI-native talent : clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.
Employment
Workplace Hybrid
Location 231 Rue Saint-Honoré, Paris, IDF 75001, France · Hybrid
Compensation EUR 70k–95k/year + BSPCE Benefits & perks Open-source, mission AI Act, télétravail partiel. How to apply Red-teaming, tests d'alignement ou framework eval LLM.
Listed Jul 25, 2026
Updated Aug 3, 2026 Giskard is hiring a Senior AI Safety Engineer — LLM robustness on the AI quality team (Hybrid · Paris, France). This is a hands-on role in LLM & generative AI —not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
Giskard développe un framework open-source de tests qualité et robustesse pour les LLM en production — référence pour l'AI Act européen.
AI Safety Engineer (hybride Paris) : red-teaming, jeux de tests d'alignement et intégration CI pour modèles déployés chez des clients enterprise.
À propos de Giskard
Startup parisienne leader sur l'évaluation LLM et la conformité. Produit open-source adopté par banques, assurances et scale-ups IA. Équipe R&D proche des régulateurs et des benchmarks publics. Missions
Concevoir suites de tests adversariaux et métriques de dérive. Automatiser les campagnes red-team dans les pipelines ML. Documenter les livrables pour audits conformité. Stack & environnement
Python, PyTorch, LangChain, pytest, Docker, CI/CD. Hybride Paris 1er/11e ; français et anglais. Profil recherché
3+ ans ML/LLM ; expérience eval, sécurité ou MLOps appréciée. Éligibilité travail en France. Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
NLP
Text pipelines: tokenization, fine-tuning, evaluation, and guardrails for user-facing language features.
✓ You can show evidence in your application: Red-teaming, tests d'alignement ou framework eval LLM. ✓ Several of these show up in recent shipped work—not only on your CV: Python, LangChain, PyTorch, NLP, and Docker. ✓ You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals. ✓ You are comfortable with the hybrid rhythm (Hybrid · Paris, France)—onsite collaboration when it matters, deep work when remote. ✓ You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords. ✓ You read the full listing (comp: EUR 70k–95k/year + BSPCE, benefits in At a glance) and your expectations align. What the employer asked for
Red-teaming, tests d'alignement ou framework eval LLM.
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