Job Description

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 this 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.

The Opportunity

Genentech is seeking a highly skilled and motivated Senior Machine Learning Scientist to join the Perturbation Biology group within the BRAID (Biology Research | AI Development) department in Genentech Research and Early Development (gRED). Our dynamic and creative team is dedicated to developing the next generation of Machine Learning models to derive actionable insights from large-scale high-content perturbation experiments and to predict the outcome of unseen perturbations to drive experimental design. We are committed to fostering innovation through cutting-edge ML methods with real-world impact in target and drug discovery. We are looking for exceptional researchers with a demonstrated research background in Machine Learning, a passion for interdisciplinary research and technical problem-solving, and a proven ability to develop and implement research ideas. The candidate is expected to routinely publish work in top-tier Machine Learning and scientific venues.

In this role, you will:

• Design and apply Machine Learning algorithms to extract regulatory circuits and identify promising novel drug targets from high-content perturbation screens, employing model classes such as causal representation learning and geometric deep learning
• Work on and integrate a variety of different data modalities such as molecular structures, omics and genetics data, images, and text.
• Collaborate with interdisciplinary and cross-functional teams including biologists, chemists, data scientists, and other stakeholders.
• Build and scale Machine Learning techniques to massive datasets and aid in the deployment of novel Machine Learning algorithms.
• Publish in top-tier ML venues and/or scientific journals, present results at internal and external scientific venues, conferences, and workshops.

Who you are

• Educational Background: PhD degree in quantitative field (e.g., Computer Science, Statistics, Mathematics) or in the physical or life sciences (e.g., Chemistry, Biology) with a strong quantitative focus.
• Experience:
  
  • Proven track record of developing and applying advanced ML models in a research or industry setting.
  • Demonstrated interest in problems across biology and chemistry as applied to the discovery and development of treatments for disease.
• Technical Skills:
  
  • Proficiency in scientific programming in Python.
  • Extensive experience with Machine Learning frameworks and libraries (e.g., PyTorch, JAX, Tensorflow).
  • Strong background in statistics, probabilistic modeling and data analysis.
• Soft Skills: Excellent communication, collaboration, and problem-solving skills.
• Publications: Strong publication record and experience contributing to research communities, including conferences like NeurIPS, ICML, ICLR, CVPR, ICCV, etc.

Preferred

• Practical experience in one or more of the following areas:
  
  • Multimodal data integration, in particular between omics data and prior knowledge in the form of GWAS, clinical patient data, or scientific literature
  • Modeling perturbation effects on heterogeneous cell states in transcriptomics data
  • Familiarity with high-content perturbation datasets (e.g., transcriptomics or image data)

Relocation benefits are available for this posting

The expected salary range for this position based on the primary location of California is $167,000 - 310,800. 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.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.

Job Tags

Local area, Worldwide, Relocation package,

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