Das ist der Job
Giotto.ai is a Switzerland-based AI company building intelligence systems for Switzerland and Europe.
Darum lohnt es sich
Giotto.ai has demonstrated strong research performance in international AI benchmarks such as ARC-AGI and is now building the next generation of production AI systems. Depending on seniority, the role may involve leading a feature squad or a project team, although strong senior individual contributors are equally valued.
Core Technologies Python PyTorch Hugging Face ecosystem Large Language Models (LLMs) Distributed computing Ray Docker Kubernetes Nice to Have CUDA programming GPU kernel optimization Experience leading technical projects or engineering teams PhD in hard sciences Ex FAANG (or similar large tech companies) Medallist in prestigious Olympiad competitions (e.g.
IMO, IOI, IPhO, or equivalent national/international competitions) 5 years+ experience in NLP/ML application Required Profile Mid-senior or senior Machine Learning Engineer (3+ years in NLP/ML) Strong Python and PyTorch experience Experience building or deploying ML systems in production Familiarity with LLMs and transformer architectures Experience with distributed training or inference systems Comfortable working in fast-moving research and production environments Location & Work Style We offer a full-time employment in Switzerland.
Hybrid model: Remote work fully supported Team gathers one week per month in the Swiss office Exceptional candidates residing elsewhere in Europe may be considered. #J-18808-Ljbffr Our mission is to build AI capabilities that enable Switzerland and Europe to preserve their strategic independence, cultural identity, and core values while achieving world-class performance in advanced reasoning systems.
Our technology focuses on new architectures for reasoning and efficient AI systems. Role Overview We are looking for a strong Machine Learning Engineer to help design and deploy advanced AI systems focused on reasoning and scalable inference.
You will work on large-scale machine learning systems, distributed training pipelines, and next-generation architectures for reasoning models.
