Principal Applied AI Scientist, Translational AI Lab (TRAIL)

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

The Opportunity

The Translational AI Lab (TRAIL) is a team within AI Biology & Translation (AIBT) focused on applying and adapting state-of-the-art AI methods to solve key challenges in disease biology, target discovery, and translational research. TRAIL works in close collaboration with BRAID (Biology Research AI Development) and therapeutic area partners to integrate models into real-world scientific workflows. We are seeking an exceptional Principal Scientist with a strong foundation in computational, statistical, and data science, and a passion for translating technical advances into biological and clinical impact. You’ll work across large, multimodal datasets and help evaluate, adapt, and deploy AI models to advance scientific questions in early discovery and translational contexts.

• Apply and fine-tune foundation models—such as large language models (LLMs), generative models, and multimodal encoders—for biological annotation, knowledge extraction, and biomarker hypothesis generation.

• Design workflows and pipelines that integrate model outputs with real-world biological data (e.g., gene expression, perturbation screens, clinical biomarkers).

• Evaluate model performance, robustness, and interpretability in collaboration with BRAID and therapeutic scientists.

• Build tools and interfaces (e.g., notebooks, dashboards, chat-based validation flows) that connect AI capabilities with experimental and translational use cases.

• Contribute to internal benchmarking, testing, and validation frameworks that enable scientific and strategic decision-making.

• Collaborate across diverse teams of biologists, modelers, and software engineers to translate AI capabilities into program-level insights

Who you are

• PhD (or equivalent) in a quantitative discipline (e.g., Computational Biology, Computer Science, Machine Learning, Mathematics, Statistics, Physics) or in a biological field (e.g., Oncology, Immunology, Molecular Biology) with demonstrated computational fluency.

• Minimum of 5 years of post-PhD experience in drug discovery, translational biology, or related computational science fields.

• Consistent and sustained record of scientific excellence in high-impact publications

• Thought leadership in a relevant scientific domain

• Proven ability to lead and influence cross-functional projects.

• Strong mentoring record and demonstrated investment in talent development.

• Recognized as a domain expert and innovator, capable of shaping scientific direction and organizational strategy.

• External collaborations, invited talks, and involvement in scientific communities are valued.

Preferred Experience

• Exposure to biomedical or multiomic data (e.g., single-cell, bulk RNA-seq, CRISPR screens, protein interaction networks).

• Hands-on experience with LLM-based workflows, prompt engineering, fine-tuning, or real-time retrieval and evaluation systems (e.g., RAG, AutoGen).

• Experience with benchmarking, evaluation frameworks, or model interpretability in applied settings.

• Prior involvement in translational research, target discovery, or biomarker identification is a plus but not required.
   

About AIBT and TRAIL

AI Biology & Translation (AIBT) is a department within the Computational Sciences Center of Excellence (CS-CoE). AIBT connects foundational AI development (via BRAID) with translation and application (via