Sr. Machine Learning Scientist

Genentech•Daly City, CA

2d•$168,100 - $312,300•Onsite

About The Position

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organizations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximizing these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), we are revolutionizing drug discovery with cutting-edge machine learning (ML) techniques. We are seeking a Senior Machine Learning Scientist to join the Foundation Models team within Prescient Design (gRED). In this role, you will drive the development of our internal reasoning Large Language Models (LLMs) and enable it to succeed at drug discovery tasks, including biomolecular design. You will work at the intersection of engineering and research, designing and scaling large machine learning systems.

Requirements

PhD in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
0 – 2+ years of industry or post-doc experience with a focus on deep learning.
Extensive experience developing and training large-scale machine learning models, including approaches to improve domain understanding, reasoning capabilities, and model alignment.
A strong history of research excellence at top-tier venues (e.g., NeurIPS, ICLR, ICML).
Strong software engineering skills and experience designing and operating large-scale or high-performance machine learning systems.

Nice To Haves

Experience with molecular modalities (e.g., protein sequences, chemical graphs, and structured molecular data) is highly valued but not required.
A public portfolio of research or significant contributions to open-source ML libraries.
A passion for applying frontier AI to drug discovery.

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.
Act as the primary driver of 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.
Act as a technical mentor to junior staff and interns, fostering a culture of engineering excellence and rapid experimentation.