AI Scientist

Key Skills

AI technology, Algorithms, Biomedical Imaging, Drug Discovery, Genomic data, Machine learning techniques, Proteomics, PyTorch, TensorFlow, Transcriptomics

Job Description

The Role

You will be responsible for developing and executing a research agenda around the core problems we work on at BenevolentAI - scientific discovery from structured and unstructured data sources.

BenevolentAI is committed to growing diverse and inclusive teams. We welcome you to apply if the job description as a whole appeals, even if there are specific areas where you don’t have previous experience!

‍Primary Responsibilities

• Bring expertise in the application of artificial intelligence to real-world problems in drug discovery and development
• Promote machine learning best practices, such as: scalable training and deployment, model introspection and evaluation, new state-of-the-art implementations, etc.
• Work as a member of a cross-functional team comprising specialists in informatics, engineering, AI, drug discovery and product. In these teams, you will apply the latest advances in machine learning to develop tools for drug discovery and development.
• Define the analytical and technical problems in applying artificial intelligence and machine learning to develop new solutions and deliver applied research within your team, and to influence an empirical approach across the company

‍We are looking for someone with

• An advanced degree (a PhD) in computer science or a related field with a clear focus on empirical research, or equivalent experience
• Knowledge of modern tools for ML such as Tensorflow, Pytorch, etc.
• Experience implementing machine learning workflows.
• Awareness of and passion for the state of the art in machine learning.

Preferred (not required) qualifications

• Experience building research prototypes and developing product-worthy tools from them.
• Experience with relevant research applying modern machine learning and artificial intelligence techniques to biological data (e.g., genomics, transcriptomics, epigenomics, proteomics, etc.) or clinical data (e.g., electronic health records).
• Experience with the development and evaluation of methods for biomedical knowledge base construction and machine reading.
• Experience with automated reasoning from a knowledge base, including graph inference algorithms and knowledge base completion.
• Knowledge of the pharmaceutical industry, ideally with a focus on drug discovery.