Senior / Principal Machine Learning Scientist

2w2 weeks ago

Genentech

New York City, US · Full-time · $160,100 – $194,000

About this role

Advances in AI and computational sciences are transforming drug discovery at Roche's Research and Early Development organizations. The Computational Sciences Center of Excellence harnesses data and AI to deliver innovative medicines. Join the AI for Drug Discovery group at Prescient Design in gRED.

Revolutionize drug discovery with cutting-edge machine learning as a Senior or Principal Machine Learning Scientist on the Foundation Models team. Drive development of internal reasoning Large Language Models for drug discovery tasks like biomolecular design. Work at the intersection of engineering and research, scaling large ML systems.

Lead design of scientific reasoning systems, improving performance on long-horizon reasoning and decision-making. Translate biological and chemical knowledge into ML objectives with domain experts. Architect distributed systems for efficient training and evaluation workflows.

Partner with cross-functional teams to transition models from prototypes to production for active discovery programs. As Senior, own training runs and mentor juniors; as Principal, define roadmaps and guide initiatives across gRED. Contribute to seamless data sharing between gRED and pRED.

Requirements

Expertise in large language models and scientific reasoning capabilities
Experience designing and training foundation models for complex tasks
Proficiency scaling distributed machine learning systems for large datasets
Knowledge of biomolecular design and drug discovery applications
Ability to integrate biological and chemical domain knowledge into ML frameworks
Track record developing evaluation methodologies for reasoning tasks
Skills in engineering robust ML pipelines from research to production

Responsibilities

Lead the design and evolution of scientific reasoning systems, setting technical direction for model architectures, training strategies, and evaluation methodologies
Define and execute approaches to systematically improve model performance on scientific tasks, including long-horizon reasoning and complex decision-making
Translate biological and chemical domain knowledge into machine learning objectives, training signals, and evaluation criteria, working closely with domain experts
Architect and improve large-scale distributed machine learning systems, ensuring robustness, efficiency, and reproducibility across training and evaluation workflows
Partner with researchers and cross-functional teams to move models from research prototypes to production-ready systems that support active discovery programs
Drive technical implementation for scientific reasoning, translating high-level research goals into robust training code
Own the end-to-end integrity of large-scale training runs, from data orchestration to the development of rigorous reasoning benchmarks