Giotto.ai korporativ foydalanish uchun suveren fikrlash texnologiyasini ishlab chiqadigan Shveytsariyaning sun’iy intellekt kompaniyasidir. Kompaniya taklifi bitta GPU’da ishlashga mo‘ljallangan, yuqori darajadagi fikrlash qobiliyatiga ega Giotto 1 modeli bilan yordamchi, hujjatlar va kod bilan ishlash vositalari, integratsiyalar, agentlar hamda monitoringni birlashtiradigan sun’iy intellekt operatsion tizimini jamlaydi. Tashkilotlar platformadan Giotto’ning Yevropa buluti orqali foydalanishi, uni xususiy GPU’larga joylashtirishi yoki Giotto’ga tayyor sertifikatlangan apparat qurilmalaridan foydalanishi mumkin. Kompaniya tartibga solinadigan va texnik jamoalarga ma’lumotlar joylashuvi, infratuzilma hamda xususiy sun’iy intellekt joylashtirmalari ustidan ko‘proq nazorat berishga e’tibor qaratadi.
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.
Tavsif
- 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
Talablar
- 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
Imtiyozlar
- Hybrid work arrangement
- Regular team collaboration at the Lausanne office