Roche is looking to solve the problem of accelerating R&D in drug discovery and development by harnessing the power of data and Artificial Intelligence (AI) to deliver more innovative and transformative medicines for patients worldwide.
Requirements
- Strong technical foundation in deep learning and probabilistic modeling, with demonstrated project or publication experience.
- Experience building and deploying ML/AI pipelines using Python, PyTorch, HuggingFace, and/or JAX; familiarity with tools like LangChain, Streamlit, or MLFlow is a plus.
- Able to adapt and apply existing AI models (e.g., LLMs, encoders, transformers) in a rigorous, reproducible way to new biological domains.
- Comfort working with biological or clinical data types—or strong interest in learning and collaborating closely with domain experts.
- Experience with benchmarking, evaluation frameworks, or model interpretability in applied settings.
- Hands-on experience with LLM-based workflows, prompt engineering, fine-tuning, or real-time retrieval and evaluation systems (e.g., RAG, AutoGen).
- Exposure to biomedical or multiomic data (e.g., single-cell, bulk RNA-seq, CRISPR screens, protein interaction networks).
Responsibilities
- 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.
Other
- Ph.D. or M.S. (with relevant experience) in Machine Learning, Computer Science, Data Science, Computational Biology, or a related quantitative field.
- 0-3 years of relevant experience in AI, machine learning, or computational biology for the Scientist level, and 3-6 years for the Senior Scientist level.
- Excellent communicator who can collaborate in multi-disciplinary settings and explain technical results to scientific partners.
- Relocation benefits are available for this job posting.
- Genentech is an equal opportunity employer and provides accommodations for applicants with disabilities.
