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We work, where others go on vacation.

Alpamayo is based in QUBO, a new innovation hub in Sarnen in Central Switzerland. It's a 30-minute commute from Lucerne. With one of our co-founders based in Aachen, Germany, working from the Digital Church is also an option.



Let’s shape the future of predictive maintenance together!

Working at Alpamayo means being part of a small team, who are taking on a big challenge. We learn fast. We move fast. We grow fast.

Working Student or Internship


Sarnen / Aachen

Advance our UI & UX development using VueJS and Typescript. Craft interactive data visualizations, design intuitive interfaces, and collaborate closely with our backend team to ensure harmonious integration.

Working Student / Intern

Marketing & Business Development

Aachen / Hybrid

Embark on a Journey with Alpamayo: Delve deep into the world of machine manufacturing with a focus on marketing and business development. As a working student, you'll gain hands-on expertise guided by our CMO, Benedikt. Unravel industry trends, strategize outreach, and contribute to our mission of transforming predictive maintenance. Become an integral part of a passionate team shaping tomorrow's sustainable production systems. Are you up for the challenge?

Bachelor's/Master's Thesis

Data Science & Engineering

Sarnen / Aachen

The most advanced predictive analytics solutions effectively unify cutting-edge AI methods and specific process expertise. Your engineering background is a crucial ingredient to contextualize data and improve the effectiveness of our AI solutions. You'll be tasked in developing methods to ingest that knowledge into our machine learning algorithms.

Working Student or Internship

Machine Learning Engineer

Sarnen / Aachen

Drive our machine learning pipelines and data science projects forward. Our focus spans from research to production, including graph neural networks, motif discovery, conformal prediction, Topological Data Analysis, and a variety of anomaly detection methods for industrial time-series analytics. A comfortable proficiency in PyTorch and common libraries for time-series analytics is vital.

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