Intern in Machine Learning for Antibody-Antigen Complex Scoring (6 months)

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections,  where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

The Position

AIDD (AI for Drug Discovery), part of Roche Computational Sciences Center of Excellence, is an interdisciplinary team applying machine learning to discover and engineer biologic drugs. Within the LMDD (Large Molecule Drug Design) team, we focus on developing next-generation therapeutics, particularly therapeutic antibodies, using innovative computational methods combined with biological expertise. Key areas include de novo design, structure prediction, affinity maturation, and accurate scoring of macromolecular complexes to enhance drug discovery capabilities.

The Opportunity 

• Develop a state-of-the-art multi-modal scoring function designed to bridge the gap between geometric deep learning and physics-based energy approximations.
• Take responsibility for creating and implementing innovative methods to rank generated antibody-antigen complexes.
• Collaborate closely with international, cross-functional teams across our key global sites in Basel, New York, and San Francisco.
• Drive high-level research initiatives with the clear goal of steering results toward a formal scientific publication.
• Act as a highly motivated intern within a fast-paced environment, merging advanced computational techniques with drug discovery efforts.

Who You Are

• Currently enrolled as a Master’s student in a technical field, such as Bioinformatics, Computer Science, Physics, or a related quantitative discipline.
• Strong foundational knowledge of machine learning, with a specific focus on geometric and spatial representation learning.
• Hands-on experience with modern deep learning frameworks, with a strong preference for PyTorch.
• Familiarity with the application of Machine Learning within the field of Structural Biology.
• Excellent communication and collaboration skills, with the ability to work effectively in international, cross-functional teams.
• Proficiency in English, with strong verbal and written communication skills suitable for a professional and academic environment.

Additional Information

• The start date for the internship is earliest in April or May 2026, with a duration of 6 months. 
• This position is based in Basel, Switzerland.
• The role requires a commitment to full-time employment.
• Thesis writing is not included in the internship, it focuses on practical experience. 
• Please upload your CV, a motivation letter with your application, and a certificate of enrolment (if you are currently studying). 
• For applicants from outside the EU/EFTA without a residence permit: a mandatory letter from the university is required, including duration, and a valid enrollment certificate.

Who we are

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.

Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.