Giotto.ai is a Swiss artificial intelligence company building sovereign reasoning technology for enterprise use. Its offering combines Giotto 1, a high-reasoning model designed to run on a single GPU, with an AI operating system that brings together an assistant, document and code tools, integrations, agents, and monitoring. Organizations can access the platform through Giotto’s European cloud, deploy it on private GPUs, or use certified Giotto-ready hardware appliances. The company focuses on giving regulated and technical teams greater control over data residency, infrastructure, and private AI deployments.
Junior AI Researcher at Giotto.ai — Hybrid, Lausanne
Join Giotto.ai as a Junior AI Researcher in a hybrid Lausanne role. Train and evaluate advanced models, develop benchmarks, and help turn research ideas into production-ready AI systems.
Description
- Review current AI research and turn relevant ideas into practical experiments
- Train, fine-tune, and assess Transformer-based models
- Support research on reasoning, synthetic data, model adaptation, and evaluation
- Create benchmarks and investigate model performance and failure patterns
- Test ways to improve training data, objectives, prompts, and inference strategies
- Develop clear, reproducible research software
- Record experiments, findings, and conclusions
- Work with research, engineering, and product teams
- Build understanding of how advanced research becomes production-ready AI systems
Requirements
- Completed or nearly completed a master’s degree in computer science, artificial intelligence, machine learning, mathematics, physics, or another quantitative discipline
- Strong foundation in machine learning and deep learning
- Hands-on Python and PyTorch experience from academic projects, a thesis, research, or an internship
- Working knowledge of Transformer architectures and large language models
- Understanding of probability, statistics, linear algebra, and optimization
- Ability to interpret research papers and implement their proposed methods
- Intellectual curiosity, analytical ability, and motivation to learn
- Effective communication and a collaborative approach
- Experience with Hugging Face Transformers or Datasets
- A master’s thesis focused on LLMs, reasoning, multimodal learning, reinforcement learning, interpretability, or generative AI
- Experience with model fine-tuning, LoRA/PEFT, synthetic data, or evaluation workflows
- Research publications, open-source work, or relevant personal projects
- Familiarity with GPUs, experiment-tracking platforms, or cloud infrastructure
Benefits
- Hybrid work arrangement
- Regular team collaboration at the Lausanne office