Machine Learning Engineer

Lugano, min. 80% on site

Hard requirements (please DO NOT APPLY if you don't match all)

• You should have EU or Swiss nationality/C permit.
• You should be top 5% both at your Bachelor and at your Master.
• You should upload your full transcripts.

Role

As Machine Learning engineer in Artificialy, wyu will work closely with our clients and project managers to design solutions, process data, train AI models, and build production ready AI applications. You will be involved in the full development process of AI products, to build the optimal solution for every situation.

You will collaborate with other machine learning and software engineers and infrastructure team to ensure smooth integration.

Your strong interpersonal skills will enable you to engage effectively with clients, both in-person and remotely.

Required Skills

• 2+ years of professional experience in software development or related fields.
• Strong programming skills (Python, C++, SQL, …) with proven ability to write clean, maintainable, and scalable code, well integrated into modular architectures.
• In-depth knowledge of ML libraries and frameworks (e.g. TensorFlow, PyTorch, Scikit-learn, …).
• Competences in deployment and orchestration tools (Docker, Kubernetes, CI/CD).
• Familiarity with Git-based workflows (GitLab, GitHub, Bitbucket).
• Experience with modern Python web frameworks (e.g. FastAPI, Flask).
• Experience in software engineering best practices, including testing, versioning, and structured documentation.
• Knowledge of system architecture, databases (e.g. PostgreSQL), and cloud platforms (AWS, Azure, GCP) is a plus.
• Experience in optimization and operations research is a plus.
• Background in manufacturing or engineering domains is a plus.
• Strong communication and collaboration skills.

Desirable Skills

• English: Strong command.
• Strong command of Italian or German; French is a plus.

We offer

• Full-time permanent contract.
• Fast career development, attractive compensation.
• Stimulating scientific context and informal environment.
• On-going training, mentoring, and close relationship with cutting-edge research teams.