Senior ML Scientist - AI for Biology & Translation

F. Hoffmann-La Roche AG; South San Francisco (CA); On-site; USD 148,000 - 274,000; Full time

Benefits offered by this job

Discretionary annual bonus

Competitive compensation package

Job summary

Genentech, a Roche subsidiary, seeks a Senior Machine Learning Scientist to lead AI-driven biology projects in translational science. You will shape model development, collaborate across multidisciplinary teams, and translate cutting-edge AI into impactful biomedical capabilities.

You will contribute to foundation models, multimodal AI, and scalable pipelines while engaging with researchers to accelerate drug discovery and biomarker development.

Qualifications

• Ph.D. in Machine Learning, Computer Science, AI, computational biology, statistics, or related field.
• Strong publication record or significant technical contributions in ML/AI.

Responsibilities

• Develop and apply state-of-the-art ML methods to biology and translational research.
• Develop, adapt, and evaluate foundation models, LLMs, multimodal models, and generative AI for biomedical use.
• Design scalable ML workflows integrating diverse datasets (omics, imaging, clinical).
• Lead efforts to optimize, benchmark, validate, and interpret AI models for science.
• Collaborate with computational scientists, software engineers, biologists, clinicians, and domain researchers.
• Establish best practices for model development, evaluation, reproducibility, and deployment.

Skills

Deep learning

Foundation models

Generative AI

Large language models

Multimodal learning

Research-to-deployment

Education

Ph.D. in Machine Learning or related field

Tools

PyTorch

JAX

TensorFlow

Hugging Face

Job description

Genentech, a Roche subsidiary, seeks a Senior Machine Learning Scientist to lead AI-driven biology projects in translational science. You will shape model development, collaborate across multidisciplinary teams, and translate cutting-edge AI into impactful biomedical capabilities.

You will contribute to foundation models, multimodal AI, and scalable pipelines while engaging with researchers to accelerate drug discovery and biomarker development.