Junior ML Engineer: Production AI, Hybrid in Switzerland

Startupvalleys; Lausanne; Hybrid; CHF 90.000 - 120.000; Vollzeit

Benefits dieser Stelle

Hybrid work model

Zusammenfassung

Giotto.ai in Switzerland is seeking a Junior Machine Learning Engineer (Master's student or recent graduate) to build, test and deploy ML systems based on transformers and reasoning models. You will turn research prototypes into reliable software, working with data pipelines, APIs, and GPU infrastructure while ensuring production readiness.

You will collaborate with researchers and engineers to deploy AI workloads across cloud, on‑premises, and hybrid environments, learning about model

Qualifikationen

• Master's degree in computer science, artificial intelligence, machine learning, software engineering or related field.

Aufgaben

• Develop and maintain Python components for machine learning applications.
• Integrate, fine-tune and evaluate Transformer-based models.
• Help build data-processing, training and inference pipelines.
• Support deployment of models into reliable applications and services.
• Measure and improve model performance, latency and resource usage.
• Write tests and contribute to code quality, documentation and reproducibility.
• Investigate technical problems across models, software and infrastructure.
• Work with researchers to turn experimental code into maintainable systems.

Kenntnisse

Python

PyTorch

LLMs familiarity

Software engineering fundamentals

Testing

Version control

Collaborative mindset

Ausbildung

Master's degree in CS/AI/ML or related field

Tools

Hugging Face

Docker

Kubernetes

Linux

APIs

Jobbeschreibung

Giotto.ai in Switzerland is seeking a Junior Machine Learning Engineer (Master's student or recent graduate) to build, test and deploy ML systems based on transformers and reasoning models. You will turn research prototypes into reliable software, working with data pipelines, APIs, and GPU infrastructure while ensuring production readiness.

You will collaborate with researchers and engineers to deploy AI workloads across cloud, on‑premises, and hybrid environments, learning about model