Senior/Principal Machine Learning Scientist, Structure and Simulation (AI for Drug Discovery)

At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), we are developing models to drive a step-change in machine learning for drug discovery. We are interested in building models that transform drug discovery from a balkanized process -- different models for every drug modality; different models for structure-based hit finding versus ligand-based lead optimization; simulation methods for high-concentration properties that can't interact with black-box methods for affinity -- into a unified one.

We are looking for exceptional, experienced machine learning scientists with strong engineering abilities who want to perform high quality research at the intersection of machine learning and biology that have direct impact in therapeutic discovery.

• Develop novel machine learning methods to answer challenging research questions in large molecule drug discovery
• Work with biological and chemical data from heterogeneous sources
• Contribute to an initiative to consolidate projects in machine learning theory into a single coherent model for lab-in-the-loop drug discovery

• Develop novel machine learning methods to answer challenging research questions in large molecule drug discovery
• Work with biological and chemical data from heterogeneous sources
• Lead an initiative to consolidate projects in machine learning theory into a single coherent model for lab-in-the-loop drug discovery

• Significant education in computer science or the life and physical sciences, or equivalent work experience: for example, anything from a BS+7 to PhD+2 years, with experience designing and building machine learning systems, particularly for molecules and biological sequences.
• Demonstrated experience with Python and deep learning libraries such as PyTorch, TensorFlow, or JAX.
• Familiarity with areas of modern machine learning research, such as reinforcement learning, sampling, and multimodal representation learning.
• Demonstrated research experience, including at least one first author publication or equivalent.
• Strong communication and collaboration skills
• Portfolio of computational projects (available on e.g. GitHub)

• Significant education in computer science or the life and physical sciences, or equivalent work experience: for example, anything from a BS+10 to PhD+5 years, with experience designing and building machine learning systems, particularly for molecules and biological sequences.
• Demonstrated experience with Python and deep learning libraries such as PyTorch, TensorFlow, or JAX.
• Familiarity with areas of modern machine learning research, such as reinforcement learning, sampling, and multimodal representation learning.
• Demonstrated research experience, including at least one first author publication or equivalent.
• Strong communication and collaboration skills
• Portfolio of computational projects (available on e.g. GitHub)

Relocation benefits are NOT available for this job posting

The expected salary range for this position, based on the primary location of San Francisco, is $147,800 - 274,400 of hiring range for the Senior Scientist, and $172,400 - 320,200 for the Principal Scientist. For the primary of location of New York City, $141,300 - 262,500 for the Senior Scientist, and $164,900 - 306,300 for the Principal 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.

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