As an ML Research Fellow you’ll work on developing novel ML models as well as helping with routine ML support of our ongoing therapeutics programs. Applications include multi-modal models of antibody biophysical properties, de novo and structure driven protein design, better protein language models, and active learning and bayesian optimization methods for embedding these models in our design-build-test loop, amongst many others. You’ll be mentored by an experienced ML scientist from our team and work closely with an interdisciplinary team of engineers, wet-lab scientists and drug developers to ensure your work is relevant for active drug development programs.

Key Responsibilities

• Identify, evaluate, and deploy the right models and sequence engineering methods within our weekly antibody design-build-test workcell.
• Develop and evaluate novel ML models or sequence optimization approaches to solve antibody engineering challenges relevant to BigHat’s therapeutics programs.
• Support model building, active learning, and drug development efforts for ongoing BigHat partnerships.
• Work with an interdisciplinary team of biologists, data scientists and machine learning scientists to gain sufficient domain familiarity to ensure your work is impactful.
• Work within, and contribute to, a production-grade codebase and associated ML Ops infrastructure to maintain high levels of automation for existing and new models.
• Document and present the results of your efforts to the relevant BigHat departments.

Skills Knowledge And Expertise

• BS, MS, or PhD degree in ML, CS or in the hard sciences with significant ML experience and a strong math and prob/stats background.
• Strong competency in Python, familiarity with PyTorch, exposure to modern software engineering best practices.
• Strong communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
• Nice-to-haves include experience with protein structure modeling, de novo design, familiarity with antibody biology, and experience training and deploying models on AWS.