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Rollomatic SA

Rollomatic is a private Swiss company specializing in the design and manufacturing of high precision CNC machines for production grinding of cutting tools, cylindrical grinding, and laser cutting of ultra-hard materials. We are customer-oriented and we provide complete manufacturing solutions for the grinding of precision tools and parts, including wheel dressing and measuring finished parts. Our manufacturing methods and our precision assembly are based on our passion for Swiss engineering and craftsmanship. Our philosophy is demonstrated by our teams in all stages over the life of the products, from design, through assembly and Lean Manufacturing, and all the way to service and client advice and support. We strive to build a relationship of trust and cordiality with our customers in a spirit of partnership. Our 12,000 m² production site located in Landeron incorporates ultra-modern manufacturing equipment and technology. Sustainable development is part of our values— photovoltaic panels cover all of our buildings and produce 50% of our own energy consumption.
09/05/2025
Full time
EPFL Master Internship Data Science (M/W) 100%
Rollomatic SA Hybrid (2525 Le Landeron, Neuchâtel, Switzerland)
The cutting tools necessary for manufacturing smartphones, tablets, aircraft, cars and medical devices are high-tech products made on high-precision machines with high-performance software. Rollomatic designs, manufactures, markets, and maintains these production systems worldwide with nearly 400 employees. THEME OF THE WORK: “DATA SCIENCE TO IMPROVE INDUSTRIAL PROBLEM SOLVING” YOUR PROFILE: Currently pursuing a Master’s degree in Data Science, computer science or related fields, looking for a full-time position as an intern Practical experience with popular Natural Language Processing libraries Strong coding proficiency in Python Motivation for engineering application in the heart of Swiss-made high-precision industry YOUR MISSION: Investigate cutting-edge LLMs for domain-specific translations Enhance model performance using retrieval methods and resource-efficient fine-tuning techniques Assess...