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Research Scientist — LLM multimodal
Kyutai
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.
4 skills 2 tools
Employment
Workplace On-site
Location 4 Place Jussieu, Paris, IDF 75005, France · On-site
Compensation EUR 100k–150k/year + BSPCE Benefits & perks Publications, compute recherche, équipe open-source. How to apply Publications ou preprint LLM/audio avec code reproductible.
Listed Dec 25, 2026 Kyutai is hiring a Senior Research Scientist — LLM multimodal on the Fundamental research team (On-site · 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.
Kyutai est un laboratoire open-source français dédié aux LLM multimodaux et à l'audio (Moshi) — recherche fondamentale et publications depuis Paris 5e .
Research Scientist LLM (sur site Jussieu) : concevoir, entraîner et évaluer des modèles speech-to-speech et multimodaux avec compute de recherche et culture open science.
À propos de Kyutai
Labo indépendant soutenu par des acteurs tech européens ; publications et code ouverts. Équipes à Paris sur LLM, audio et agents conversationnels. Environnement recherche avec accès GPU et benchmarks publics. Missions
Expérimenter architectures multimodales et protocoles d'évaluation reproductibles. Publier preprints et contribuer aux dépôts open-source du labo. Collaborer avec ingénieurs pour industrialiser les démonstrateurs. Stack & environnement
Python, PyTorch, CUDA, clusters GPU, tooling Hugging Face. Sur site Paris 5e ; anglais et français au quotidien. Profil recherché
PhD ou équivalent en ML/NLP/audio ; publications ou preprints solides. Éligibilité travail en France. Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
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.
Machine Learning
Practical Machine Learning experience applied to LLM features, retrieval, agents, and evaluation in production or rigorous pilots.
Kubernetes Tool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
Docker Tool
Containerized services and reproducible dev environments for ML and LLM workloads.
✓ You can show evidence in your application: Publications ou preprint LLM/audio avec code reproductible. ✓ Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, NLP, Machine Learning, and Kubernetes. ✓ 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 explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords. ✓ You read the full listing (comp: EUR 100k–150k/year + BSPCE, benefits in At a glance) and your expectations align. What the employer asked for
Publications ou preprint LLM/audio avec code reproductible.
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Research Scientist — LLM multimodal
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