Principal Machine Learning Scientist, (gCS, BRAID)

$188,200 - $349,400/Yr

Genentech - South San Francisco, CA

Full-time - Principal

South San Francisco, CA

Chemical Manufacturing

About the position

Genentech is seeking a highly skilled and motivated Principal Machine Learning Scientist to join the BRAID team (Biology Research | AI Development) within our Computational Sciences organization. This role is pivotal in building novel machine-learning methods aimed at enhancing drug development and clinical trial design, particularly focusing on early-stage clinical trials. The successful candidate will be responsible for developing innovative machine learning methods for multimodal clinical data, including generative models and representation learning. By leveraging their expertise, the candidate will work towards improving trial efficiency, patient outcomes, and overall trial success rates. The role also involves a commitment to publishing work in top-tier Machine Learning and scientific venues, contributing to the advancement of the field. The Principal Machine Learning Scientist will lead research initiatives that intersect machine learning, biology, and clinical sciences. This includes conceiving and leading collaborations both internally and externally, maintaining expertise at the forefront of multiple areas in machine learning and biology/clinical sciences, and collaborating with cross-functional teams, including chemists, biostatisticians, clinical scientists, and data engineers. The candidate will also be responsible for the design and execution of clinical trials by developing foundation models using multimodal pre-clinical and clinical datasets. Mentorship of junior scientists and contributing to the strategic direction of the ML group within the Computational Science Organization are also key aspects of this role.

Responsibilities

• Lead research initiatives at the intersection of machine learning, biology, and clinical sciences.
• Conceive and lead collaborations both internally and externally.
• Maintain expertise at the forefront of multiple areas in machine learning and biology/clinical sciences.
• Collaborate with cross-functional teams, including chemists, biostatisticians, clinical scientists, and data engineers, to integrate ML methods into clinical trial development.
• Lead and contribute to the design and execution of clinical trials by developing foundation models using multimodal pre-clinical and clinical datasets.
• Publish research findings in top-tier machine learning and scientific journals and present at leading conferences.
• Mentor junior scientists and contribute to the strategic direction of the ML group within the Computational Science Organization.

Requirements

• Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, or a related field with a strong emphasis on machine learning.
• Proven track record with 2+ years of experience developing and applying advanced ML models in a research or industry setting.
• Proficiency in scientific programming languages such as Python and familiarity with MLOps workflows.
• Extensive experience with machine learning frameworks and libraries (e.g., JAX, PyTorch, Tensorflow).
• Strong background in statistics, probabilistic modeling, and data analysis.
• Understanding of clinical trials, drug development, and biological data.
• Excellent communication, collaboration, and problem-solving skills.
• Strong publication record and experience contributing to research communities.

Nice-to-haves

• Extensive track record of delivering innovative solutions in machine learning.
• Strong communication skills with the ability to effectively communicate technical concepts to both technical and non-technical audiences.
• Practical experience in reinforcement learning, geometric deep learning, representation learning, and generative learning.
• Experience in early-stage clinical trial design and execution and knowledge of regulatory requirements and standards in clinical trials.

Benefits

• Relocation benefits are available for this job posting.
• Discretionary annual bonus based on individual and Company performance.