Role

As a Machine Learning Engineer, you will partner with clients across diverse industries to design, build, and deploy production-grade ML systems that drive measurable business outcomes.

You will contribute across the full ML lifecycle — from exploratory modeling and experimentation to scalable deployment, monitoring, and continuous improvement in live environments.

Typical initiatives include:

• Forecasting and predictive modeling.
• Anomaly and event detection.
• Classification and user segmentation.
• Automated decision-support systems.
• Intelligent / agent-driven analytical workflows.

You will work closely with software, platform, and domain teams to embed ML solutions into enterprise architectures, ensuring reliability, scalability, and real-world impact.

Success in this role requires both strong technical execution and the ability to communicate effectively with stakeholders across technical and business functions, in both onsite and distributed environments.

Required Skills

• MSc or PhD in Computer Science, Informatics, or a related quantitative discipline.
• Strong programming skills in Python and SQL, with hands-on experience using ML libraries such as TensorFlow, PyTorch, or Scikit-learn.
• Solid understanding of machine learning fundamentals, statistical modeling, and modern data-processing techniques.
• Experience working with Git-based development workflows (e.g. GitHub, GitLab, Bitbucket).
• Experience developing REST API services (e.g. using FastAPI or similar frameworks).
• Professional proficiency in English.

Desirable Skills

• Experience with model deployment and orchestration tools (e.g. Docker, Kubernetes, CI/CD pipelines).
• Understanding of data platforms and architectures (e.g. PostgreSQL, distributed processing frameworks).
• Familiarity with production ML practices (monitoring, versioning, reproducibility).
• Proficiency in Italian or German.

We offer

• Full-time permanent contract.
• Competitive compensation and growth opportunities.
• A stimulating scientific environment with an informal working atmosphere.
• Ongoing training, mentoring, and close collaboration with cutting-edge research teams.