Machine Learning Scientist

The Position

AI Biology & Translation (AIBT) develops and applies state-of-the-art artificial intelligence to accelerate biomedical discovery across Research and Early Development in Genentech & Roche. We combine advances in foundation models, multimodal machine learning, and large-scale biological data to advance target discovery, disease understanding, biomarker development, and translational science. We are seeking a (Senior) ML Scientist with deep expertise in modern machine learning to lead the development and application of next-generation AI technologies for designing DNA and RNA sequences for nucleic acid-based medicines. We work closely with stakeholders in Cell Therapy, Gene Therapy, and Vaccine Oncology on diverse projects, ranging from platform development for lab-in-the-loop refinement of sequence designs to lead optimization for portfolio projects. Representative work includes designing regulatory elements to confer cell-type-specific transcription and engineering synonymous coding sequences to enhance translational efficiency. As a Scientist / Senior Scientist, you will directly lead sequence design campaigns, translating biological questions into predictive and generative models, and driving iterative sequence optimization in close collaboration with wet-lab experimentalists.

The Opportunity

• Build, train, and fine-tune machine learning algorithms to optimize nucleic acid-based medicines for active portfolio projects
• Design candidate sequence libraries for lab-in-the-loop optimization cycles, analyze experimental readouts (e.g., MPRA, NGS), and iteratively update designs
• Collaborate cross-functionally with wet-lab scientists and project leads to understand therapeutic constraints and tailor design algorithms accordingly
• Present technical findings, candidate designs, and model performance metrics to cross-functional team members and stakeholders
• Stay current with advancements in biological sequence modeling and nucleic acid therapeutics to bring state-of-the-art methods into our pipeline
• Contribute to scientific publications

Who You Are

• Ph.D. in a quantitative discipline (Bioinformatics, Computational Biology, Computer Science, Machine learning, or a related field) with 0-4 years of post-doctoral or industry experience
• Demonstrated research impact at the interface of machine learning and molecular biology, evidenced by publications in top-tier journals or ML conferences
• Strong Python programming skills and proficiency in deep learning frameworks such as PyTorch
• Hands-on experience developing, training, or fine-tuning sequence-to-function models for biological molecules (preferably DNA/RNA)
• Excellent communication skills in English, with the ability to speak to both computational and experimental scientists

Preferred Qualifications

• Prior experience applying machine learning in a biotech, pharma, or industry setting
• Domain knowledge in nucleic acid-based therapeutics (e.g., mRNA design, AAV capsid/promoter engineering, cell therapy vectors)
• Experience with advanced ML frameworks relevant to sequence design, such as generative modeling (diffusion, autoregressive sequence models, masked language models), active learning, model interpretability, or uncertainty quantification
• Familiarity with high-throughput functional genomics data processing (e.g., MPRA, RNA-seq, ribosome profiling)