Giotto.ai
Giotto.ai
Giotto.ai ir Šveices mākslīgā intelekta uzņēmums, kas izstrādā suverēnas spriešanas tehnoloģiju uzņēmumu vajadzībām. Tā piedāvājumā ietilpst Giotto 1 — augstas spriešanas spējas modelis, kas izstrādāts darbībai ar vienu grafikas procesoru, kā arī mākslīgā intelekta operētājsistēma, kas apvieno asistentu, dokumentu un koda rīkus, integrācijas, aģentus un uzraudzību. Organizācijas platformai var piekļūt, izmantojot Giotto Eiropas mākoni, izvietot to privātos grafikas procesoros vai izmantot sertificētas Giotto gatavas aparatūras ierīces. Uzņēmums koncentrējas uz to, lai regulētu nozaru un tehniskajām komandām nodrošinātu lielāku kontroli pār datu atrašanās vietu, infrastruktūru un privātiem mākslīgā intelekta risinājumu izvietojumiem.

Junior Machine Learning Engineer — Hybrid, Lausanne

Giotto.ai is hiring a junior machine learning engineer in Lausanne to develop and deploy Transformer-based AI systems. The role focuses on Python applications, data pipelines, and production-ready model services.

Apraksts

  • Build and maintain Python components for machine learning applications
  • Integrate, fine-tune, and assess Transformer-based models
  • Contribute to data-processing, training, and inference pipelines
  • Assist with deploying models as dependable applications and services
  • Monitor and optimize model performance, latency, and resource consumption
  • Create tests and support code quality, documentation, and reproducibility
  • Troubleshoot issues spanning models, software, and infrastructure
  • Collaborate with researchers to convert experimental code into maintainable systems
  • Develop an understanding of deploying AI workloads across cloud, private, and on-premises infrastructure

Prasības

  • Completed or nearly completed Master’s degree in computer science, artificial intelligence, machine learning, software engineering, or a related technical discipline
  • Advanced Python programming ability
  • Hands-on experience with PyTorch or another current machine learning framework
  • Working knowledge of large language models and Transformer architectures
  • Understanding of software engineering practices such as testing, version control, and maintainable code
  • Relevant experience from a thesis, internship, university project, open-source work, or personal project
  • Practical approach to problem-solving and willingness to work across the technology stack
  • Strong communication skills and a collaborative working style
  • Experience with Hugging Face, Docker, APIs, or Linux
  • Familiarity with vLLM, Ray, Kubernetes, or cloud GPU infrastructure
  • Experience deploying a machine learning model or application
  • Understanding of model inference, distributed computing, or performance optimization
  • Experience with CI/CD, experiment tracking, or data pipelines
  • Open-source contributions or other relevant technical projects

Priekšrocības

  • Hybrid work arrangement
  • Regular collaboration from the Lausanne office
  • Hands-on exposure to model inference, data pipelines, APIs, GPU infrastructure, and production deployment
  • Collaboration with research and engineering teams
  • Practical opportunity to build, test, and deploy AI systems
  • Learning exposure to AI workloads across cloud, private, and on-premises infrastructure

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