Scientist - AI & ML Protein and Antibody Engineering

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Na Roche, você pode-se apresentar como você mesmo, abraçado pelas qualidades únicas que traz. Nossa cultura incentiva a expressão pessoal, o diálogo aberto e as conexões genuínas, onde você é valorizado e respeitado por quem você é, e permitindo que você prospere tanto pessoal como profissionalmente. É assim que pretendemos prevenir, deter e curar doenças e garantir que todos tenham acesso aos cuidados de saúde hoje e nas gerações futuras.

Junte-se à Roche, onde cada voz é importante.### A posiçãoAdvances 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 can accelerate R&D by leveraging data and novel computational models. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities.

The newly established Computational Sciences Center of Excellence (CS CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to support scientists across pRED and gRED in delivering innovative medicines for patients worldwide.###
** The Opportunity
** We are seeking a highly skilled Scientist with strong expertise in AI and Machine Learning applied to protein and antibody structure, function and engineering. The successful candidate will work at the intersection of computational modeling, modern AI technologies, and biologics drug discovery, applying and implementing state-of-the-art ML approaches to design and optimize therapeutic proteins and antibodies.

As a Scientist in the CS CoE, you will leverage generative AI to accelerate antibody discovery, working within a unified team spanning Genentech and Roche Pharma.

This role offers a unique opportunity to bridge computational and experimental sciences and deliver transformative medicines to patients.
* Apply novel Machine Learning and Computational Biology methods to address complex research questions in Large Molecule Drug Discovery (LMDD), with a focus on protein and antibodies
* Develop, implement and deploy discriminative and generative models (e.g., diffusion models, LLMs) to predict structure, affinity, function of proteins
* Collaborate closely with cross-functional teams to integrate and clean biological data from heterogeneous sources.
* Design and engineer new de novo proteins and antibodies using state-of-the-art computational approaches for drug development purposes.
* Collaborate closely with cross-functional teams to integrate, curate, and analyze biological data from heterogeneous sources.
* Facilitate collaboration between experimental scientists and computational experts to maximize the impact of data and model sharing.
* Contribute to scientific innovation through publications, internal reports, and presentations at internal and external scientific venues.###
** Who You Are
*** PhD in Computational Biology, Protein Engineering, Machine Learning, Computer Science, or a related discipline.
* Expert-level understanding of protein and antibody structure–function relationships, enabling rational design and engineering decisions grounded in structural, biophysical, and functional principles.
* Strong hands-on experience in machine learning and deep learning, with the ability to independently develop, train, and evaluate models.
* Proven ability to work at both application and implementation levels, including adapting state-of-the-art models to drug discovery problems and modifying architectures or training strategies as needed.
* Advanced programming expertise in Python and deep learning frameworks such as PyTorch, Tensor Flow, or JAX, applied to protein/antibody design.
* Strong scientific track record, including first-author publications in high-impact journals or conferences.
* Excellent communication skills and a collaborative mindset, with the ability to bridge…