Machine Learning Scientist, Structure-Function ML, AI for Drug Discovery (AIDD)

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. ​

The Opportunity

The Structure-Function ML group in Basel within Prescient Design (AI4DD), CoE,  a division devoted to developing machine learning-based methods for de novo antibody design, seeks exceptional researchers who have a demonstrated research background in machine learning and protein structural biology and design, a passion for independent research and technical problem-solving, and a proven ability to develop and implement ideas from research into production. We are looking for a very talented Machine Learning Scientist to join Prescient Design/AI4DD. The successful candidate will contribute to our antibody design efforts, partner with biologists, technologists and drug discoverers to develop new machine learning methods for de novo protein design with special application to protein therapeutics.

In this role, you will:

• Develop cutting-edge machine learning methods for modeling biological data, focusing on structural biology.

• Deliver deep learning-based software solutions that accelerate drug discovery and therapeutic development in support of our de novo antibody design and lab-in-the-loop efforts.

• Collaborate with AI/ML scientists and form close working relationships with global research teams.

• Write structured, tested, and maintainable code while participating in proactive code reviews.

• Actively shape and contribute to our collaborative and innovative team culture.

• Partner with biologists and technologists to develop new methods for de novo protein design.

Who you are

• You hold an M.S. or PhD in Computer Science, Statistics, Physics, or a related technical field and possess 1+ years of hands-on experience designing and training machine learning models on large datasets.

• You have published on denovo antibody design in relevant journals like Nature Biotechnology, Neurips, or ICML.

• You are proficient in Python and at least one deep learning framework like PyTorch, TensorFlow, or JAX.

• You have experience with using MLOps frameworks like Hydra and Weights & Biases.

• You have a public codebase of computational denovo antibody design (available on e.g. GitHub)

• You have demonstrated experience with modern techniques, including hallucination or folding models.

• You bring prior experience or familiarity working with antibody sequence and structure data, which is a plus. 

• You are an excellent communicator, fluent in English, with a passion for driving projects in cross-functional environments.

Relocation benefits are NOT available for this job posting

The expected salary range for this position based on the primary location of New York is $141,100 - 262,100 of hiring range. 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

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