Machine Learning Scientist/Senior Machine Learning Scientist - Agents for Applied Small Molecule Drug Design, AI for Drug Discovery

Genentech; San Francisco (CA); On-site; USD 148,000 - 274,000; Full time

Benefits offered by this job

Discretionary annual bonus

Job summary

Genentech seeks a Machine Learning Scientist to develop autonomous, LLM-driven agents for small-molecule drug design. You will orchestrate ML models, physics methods, and cheminformatics tools alongside world-class chemists and biologists.

You will refine foundation models, drive scientific impact through publications and open-source work, and collaborate widely with computational researchers at Roche and Genentech.

Qualifications

• PhD or equivalent in machine learning, computer science, chemical engineering or related quantitative field.
• Hands-on experience developing LLM-driven agents for scientific workflows.
• Proficiency with ML frameworks and cheminformatics toolkits.

Responsibilities

• Design, build, and apply agentic workflows and ML models for small-molecule drug design challenges.
• Fine-tune foundation models for drug discovery topics using internal/external datasets and tools.
• Optimize agent-derived hypotheses with chemists and biologists.
• Publish publications, open-source releases, and present at conferences.
• Collaborate widely with Roche Genentech researchers and academic partners.

Skills

LLM-driven agents

Python

LangChain

PyTorch

JAX

RDKit

OpenEye

Education

PhD or equivalent in ML/CS/chemical engineering or related field

Tools

LangChain

PyTorch

JAX

RDKit

OpenEye

Job description

The Position

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche. Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations 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 maximising 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.

Join the small-molecule team within AI for Drug Discovery (AI4DD), formerly Prescient Design, at Roche and Genentech’s Computational Sciences Center of Excellence as a Machine Learning Scientist / Senior Machine Learning Scientist building agents for applied small-molecule drug design. You will develop autonomous, LLM-driven agentic workflows that orchestrate ML models, physics-based methods, and cheminformatics tools to accelerate discovery, working with world-class chemists and structural biologists.

The Opportunity:

• Design, build, and apply agentic workflows and ML models for key challenges in small-molecule drug design.
• Fine-tune foundation models for drug discovery relevant topics using internal and external datasets and tools.
• Optimize agent-derived hypotheses in close collaboration with world-class computational and medicinal chemists and structural biologists.
• Drive scientific impact through publications, open-source releases, and conference talks.
• Collaborate widely with computational and experimental researchers at Roche and with academic partners.

Who you are:

• You are experienced developing LLM-driven agents for scientific workflows and you understand how to orchestrate tools and models reliably.
• You bring strong machine-learning foundations in linear algebra, probability and optimization, with hands‑on experience with GNNs, sequence/language models and reinforcement learning.
• You are fluent in Python and modern agentic coding environments such as LangChain, ML frameworks such as PyTorch or JAX, as well as cheminformatics toolkits like RDKit or OpenEye.
• You hold a PhD or equivalent research depth in machine learning, computer science, chemical engineering or a related quantitative field such as physics or statistics, with up to 2 years of industry research experience (Scientist) or 2+ years of industry research experience (Senior Scientist).
• You have a record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects (e.g. hosted on GitHub/GitLab).

Preferred:

• Hands‑on experience orchestrating multi‑tool or multi‑agent scientific pipelines.
• Hand‑on experience working along the small molecule drug discovery value chain and an excitement to engage with chemists
• Familiarity with structural biology datasets

Relocation benefits are NOT available for this opportunity

The expected salary range for this position, based on the primary location of San Francisco, is $147,600 - $274,000 for the ML Scientist, and $167,400 - 310,800 for the Senior ML Scientist. For the primary of location of New York City, $141.100 - $262,100 for the ML Scientist, and $160,100 - 297,300 for the Senior ML Scientist. Actual pay will be determined based on experience, qualifications, geographic location, and other job‑related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits

#ComputationCoE

#tech4lifeComputationalScience

#tech4lifeAI

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

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