Machine Learning Scientist, Scientific Reasoning Models, AI for Drug Discovery

Genentech • New York, NY

7d • $141,100 - $274,000

About The Position

Roche’s AI for Drug Discovery group is seeking a Machine Learning Scientist to join the Foundation Models team within Prescient Design (gRED). The role contributes to internal reasoning large language models (LLMs) and helps enable their use in drug discovery tasks, including biomolecular design. It combines engineering and research, including the design and scaling of large machine learning systems.

The scientist executes defined projects and writes clean, efficient code to test hypotheses about reasoning and alignment. The role also contributes to training infrastructure and data pipelines, works with senior scientists to implement algorithms, and translates research papers into working prototypes. The position collaborates with researchers to move ideas and prototypes into scalable, production-ready systems and with domain experts to evaluate model capabilities for biological research.

Requirements

• Bachelor’s or master’s degree in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field, with at least 2 years of relevant work experience; or a Ph.D. with 0–2 years of relevant work experience.
• Experience developing and training large-scale machine learning models, including post-training techniques to enhance domain knowledge, reasoning capabilities, and model alignment.
• Strong research record at top-tier venues such as NeurIPS, ICLR, or ICML.
• Strong software engineering skills and experience working with high-performance computing systems.

Nice To Haves

• Experience with molecular modalities such as protein sequences, chemical graphs, and structured molecular data.
• A public portfolio of research or significant contributions to open-source machine learning libraries.
• Passion for applying frontier AI to drug discovery.

Responsibilities

• Design, implement, and improve large-scale distributed machine learning systems, including robust, performance-critical code and core infrastructure.
• Develop and execute strategies to improve model performance on scientific tasks, including long-horizon task completion and complex reasoning.
• Translate biological and chemical domain knowledge into machine learning objectives, training signals, and evaluation criteria.
• Design and implement evaluation methodologies for biological research, collaborating with domain experts to establish benchmarks and curate high-quality data.
• Collaborate with researchers to translate ideas and prototypes into scalable, production-ready systems.
• Write clean, efficient code to test hypotheses regarding reasoning and alignment.
• Help maintain training infrastructure and data pipelines so experiments run reliably on clusters.
• Work with senior scientists to implement novel algorithms and translate research papers into working prototypes.

Benefits

• A discretionary annual bonus may be available based on individual and Company performance.
• The position qualifies for benefits detailed at a link referenced in the posting; specific benefits are not listed.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level