Internship: Training and deploying AI/ML protein abundance model predictions

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

We are seeking an Intern (Data & Database Management – Multi-Omics / Protein Abundance) in Basel, Switzerland to support a translational R&D project focused on inferring protein molecule numbers across human tissues and delivering an MVP “predictability map” that clarifies when RNA is a reliable proxy for protein abundance—and when it isn’t.

As a Computational Sciences Center of Excellence (CS CoE) AIBT/CBM intern, you will collaborate with experts in ML research, computational biology and data engineering. You will leverage pharma-scale infrastructure to bridge the gap between AI innovation and significant biological discoveries. 

The new CS CoE is a unified strategic group designed to harness AI and data to help pRED and gRED scientists deliver innovative medicines faster. Within the CS CoE, the Computational Biology and Medicine (CBM) department focuses on furthering our understanding of disease, patient populations, and target biology by having access to the largest Pharma R&D datasets in the world. The CS CoE AI for Biology and Translation (AIBT) department is a multidisciplinary team of ML engineers and researchers dedicated to decoding complex biology. AIBT operates at the critical junction of biology and clinical science, employing cutting-edge machine learning and statistical methods.

The Opportunity

• In this internship, your mission is to build the data foundation that turns research outputs into an iteratively released, production-oriented resource. 

• You will help to operationalize an RNA-to-protein prediction effort that estimates tissue-resolved protein abundance from transcriptomics and proteomics data to support target assessment and patient selection.

• The primary mission is to set up robust data management and a deployment pathway so scientists can run inference reproducibly, track versions, and consume predictions in downstream tools.

• Design and implement a lightweight data model for inputs, metadata, and prediction outputs (e.g., proteomics/mass-spectrometry ground truth, RNA features, model versions).

• Enable MVP release workflows (v0.1 → v0.x) by producing stable, queryable tables/exports and clear “what changed” release notes

• Operationalize Machine Learning Algorithms: Leverage your understanding of machine learning to operationalize existing algorithms. Deploy model inference (batch and/or API), including CI/CD, monitoring, and access control aligned with internal standards.

• Create simple user entry points and ensure integration with existing and developing company tech stack. 

• Collaborate closely with computational biologists and data scientists to ensure the data layer supports both discovery and adoption

Who You Are 

• You hold a Bachelor's or Master's degree within the past 12 months, or you are currently enrolled in a Master's or PhD degree program in a relevant field (preferably in a related field such as Computer Science, Data engineering, Bioinformatics, Computational Biology, etc.)

• Solid programming skills in Python (and/or R) and confidence working with data at scale.

• Familiarity with data pipelines, reproducibility, and collaborative workflows (Git, CI/CD, code review, documentation)

• Familiarity with  containers (Docker) and cloud/HPC workflows.

• Experience with data modelling in MongoDB or other NoSQL databases

• Prior experience in mass spectroscopy readouts, protein abundance, or -omics data and common identifiers/mappings (gene/protein IDs, ontologies)

• Familiarity with Kubernetes or other deployment orchestration

• Experience using and developing REST APIs (e.g. using FastAPI, Plumber)

• Strong communication skills and enjoyment of cross-functional teamwork

• Proficiency in English (written, spoken)

Application Process

• To be considered, your application needs: a cover letter and a CV (optionally including a list of relevant publications and references). 

• Non EU/EFTA students must prove that they are enrolled at the University during the full duration of their internship, as well as prove that the internship is a mandatory part of their curriculum 

Additional Information

• This position is based in Basel, Switzerland.

• Duration: 6-9 months. The internship has to end by 31.12.2026.

Ready to take the next step? We'd love to hear from you. Apply now to explore this exciting opportunity!

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.