What you'll do:

• Hands-on role developing and implementing innovative AI methods (use of an AI model on a specific dataset for a specific purpose) under supervision as well as independently to deliver on highly visible projects
• Advanced development skills in Python and the ability to understand and internalizing AI models from published papers and repositories
• Train, test, and evaluate AI models on realistic internal and external data, and present results in an organized manner for team evaluation and input
• Develop and deliver various biological and chemical insights and outcomes from applying AI models for broader communication
• Stay up to date in the latest developments of evolving AI methods through self-learning and using on-the-job opportunities
• Understand and manage run time requirements of AI models, visualizations, and cloud-based computation to increase efficiency and quality of AI approaches
• Develop new AI models from scratch using TensorFlow or PyTorch, understanding and modifying learning objectives and training methods of the models

What makes you a great fit:

• Extensive classroom learning as well as on-the-job, hands-on experience of advanced AI models, selecting and applying suitable AI models for a given purpose.
• Experience with AI is expected not just at the level of running the code but deeper understanding models, their purpose, and the ability to interpret functioning of a model on given data
• Demonstrable deep experience in one or more of specific AI focus areas we are hiring: NLP and Generative Models (LLMs), and AI for medicinal chemistry and design of biological medicines.
• Strong skills in Python, relevant Deep Learning frameworks such Transformers, advanced AI model training methods
• Experience with high performance computing systems and cloud-based services involving large and diverse biomedical data
• Running AI model using GPU is a must and the ability to scale a model to multi-GPUs is big plus
• Strong communication skills to explain technical details, decisions, consequences, and results to senior researchers and non-data science communities
• Deep understanding of biological and chemical datasets, knowledge repositories in the areas such Gene Ontology
• A humble and curious mind that can continuously learn while recognizing and applying internal resources to the overall vision and purpose of the team.
• 1 - 3 years of deep, hands-on experience working on AI projects and either clear evidence of significant projects personally developed and delivered and/or publication track record
• 1 - 3 years of experience in working with large scale, realistic data sets and analyzing results towards specific business/scientific outcomes
• Experience in pharma, biotech, or healthcare is a big plus but strong tech background with the willingness to adapt to pharma is equally acceptable.