Senior/Principal AI Scientist, Foundational ML, AI for Biology & Translation (AIBT)

Genentech • Daly City, CA

4d • $168,100 - $373,800

About The Position

Genentech Research and Early Development (gRED) seeks a Senior/Principal AI Scientist to join the Foundation Models team (DELTA) within the AI for Biology & Translation (AIBT) department. The team conducts AI research focused on large-scale biological foundation models that can reason across biological modalities, guide experiments, and learn from outcomes to accelerate target and drug discovery.

The scientist will advance AI research in multimodal generative modeling, representation learning, LLM mid-training and post-training, agentic science, and reinforcement learning, with applications to genomics, perturbation biology, imaging, and multimodal experimental data. Working with AI scientists, AI engineers, and computational biologists, the successful candidate will act as a technical and scientific leader, shape research directions, develop and test ideas, address modeling, data, and engineering challenges at scale, lead collaborative projects, and publish in leading machine-learning and scientific venues.

At the Principal level, the role additionally defines and drives broader research initiatives, sets technical direction across related projects, and resolves critical challenges while remaining hands-on in research and development. The position is based on the primary location of San Francisco. Relocation benefits are not available. A discretionary annual bonus may be available based on individual and company performance.

Requirements

• Ph.D. in Computer Science, Machine Learning, Computational Biology, or a related quantitative field.
• A proven record of advancing AI models in research and/or industry, with deep expertise in at least one of large-scale representation learning, multimodal generative models, LLMs, AI agents, or reinforcement learning.
• Strong motivation to advance AI for science and translate AI developments into progress in biology and drug discovery.
• Excellent knowledge of deep-learning theory and practice, strong research intuition, and ability to turn open scientific questions into rigorous experiments.
• Proven experience developing and delivering innovative machine-learning solutions in the relevant areas.
• Excellent Python programming skills, fluency with modern agentic coding environments, and extensive experience with frameworks such as PyTorch or JAX.
• Strong grasp of software engineering, data engineering, and MLOps best practices, including scalable data processing, high-performance computing, and reproducible training and evaluation workflows.
• Strong publication record and active research-community contributions, including top-tier venues such as NeurIPS, ICML, and ICLR, and/or a public portfolio demonstrating substantial contributions to relevant AI projects.
• Excellent communication, collaboration, and problem-solving skills.

Nice To Haves

• Prior experience applying AI to biology, chemistry, or drug discovery, especially with multimodal experimental data or experimental design.
• Practical experience bridging innovative machine-learning methods with target or drug discovery applications.
• Experience with biological and chemical modalities and tasks such as molecular structures, single-cell/omics data, perturbation biology, and multimodal biological datasets.
• Hands-on experience developing, fine-tuning, and optimizing LLMs and agentic systems.
• For the Principal level, typically 3 or more years of post-Ph.D. research experience in relevant AI areas, or an equivalent portfolio demonstrating comparable depth, independence, and impact.
• For the Principal level, a strong record of driving complex research initiatives with substantial scientific or technical impact, including sound judgment in identifying high-value problems and making consequential technical decisions.

Responsibilities

• Design and build foundation models for target and drug discovery, focusing on large-scale representation learning, multimodal mid-training and post-training, reasoning, and reinforcement learning.
• Develop scalable methods to curate, organize, and integrate diverse data modalities—including molecular structures, biological sequences, omics data, biochemical readouts, and text—into high-quality training data and evaluation tasks.
• Develop models and agentic systems that propose experiments, interpret results, and learn from experimental feedback to advance lab-in-the-loop discovery.
• Build training and evaluation environments from biological data to develop and measure scientific reasoning and discovery capabilities.
• Scale state-of-the-art models to massive datasets, addressing distributed training, efficient experimentation, and reliable data pipelines in collaboration with engineering and MLOps teams.
• Publish in top-tier machine-learning venues and scientific journals, and present results at internal and external conferences and workshops.
• At the Principal level, define and drive research initiatives, establish technical direction across related projects, and address critical modeling, data, and engineering challenges while remaining hands-on.

Benefits

• A discretionary annual bonus may be available based on individual and Company performance.
• The position qualifies for company benefits detailed at the link provided in the job posting.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree