Machine Learning Scientist, MLDD (Large Molecule Drug Discovery)

A healthier future. Its 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. Thats what makes us Roche.

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roches 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.

The Opportunity

The Large Molecule Drug Discovery group within AI for Drug Discovery seeks exceptional researchers who have a demonstrated research background in protein design and protein structure prediction/analysis, machine learning, project execution, a passion for research collaboration and technical problem-solving, and a proven ability to implement ideas and develop and/or apply methods for large molecule drug discovery. The group provides a dynamic and challenging environment for cutting-edge, multidisciplinary research including access to heterogeneous data sources, close links to top academic institutions around the world, as well as internal Genentech Research and Early Development (gRED) partners and research units. Our mission is to develop and apply physics and machine learning based methods to design novel macromolecules with therapeutic potential. Researchers in this role will develop, manage, and apply computational approaches for large molecule property prediction and de novo generative design, leverage multi-modal data sources including protein structure, protein property, and next-generation sequencing (NGS) data towards portfolio impact.

You will play a pivotal role in supporting, applying, and advancing the next generation of l arge molecule generative machine learning models for drug discovery. We seek an individual who is not only passionate about teamwork and technical problem-solving but also has a proven track record of delivering innovative solutions in machine learning and protein engineering. Your responsibilities will include data preparation and cleaning, deploying both internal and external discriminative and generative models, and closely collaborating and communicating with colleagues in Genentechs antibody engineering department to engineer new antibody molecules using the lab-in-the-loop framework.

In this role, you will:

• Develop and/or apply novel Computational Biology/Machine Learning methods to answer challenging research questions in LMDD.

• Work with biological data from heterogeneous sources.

• Collaborate closely with cross-functional teams across gRED to solve complex problems including developing models to predict antigen-antibody affinity, antibody-antigen complex structure, and developability properties.

• Closely collaborate with drug discovery teams to contribute to the Roche large molecule portfolio, solving unique and challenging protein design and engineering tasks.

• Contribute to and drive publications and present scientific findings at internal/external venues.

Who you are

• PhD degree in Computational Biology, Structural Biology, Computer Science, Physics or related disciplines, or a MS degree in the above disciplines with 3+years of industry research experience.

• Demonstrated experience with Python and deep learning libraries such as Pytorch and/or TensorFlow and/or JAX.

• Demonstrated research experience, including at least one first author publication or equivalent.

• Publication record and experience contributing to research communities, including conferences like NeurIPS, ICML, ICLR, CVPR, ACL, etc.

• Strong communication and collaboration skills.

Additional desired qualifications:

• Experience working with data from biology, immunology or related disciplines.

• Experience with antibody structure, function, and sequence data.

• Public portfolio of computational projects (available on e.g. GitHub).

Relocation benefits are available for this job posting.

The expected salary range for this position based on the primary location of California is $147,600, - $274,000. 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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