AI Scientist Intern, Computational Protein Design

Xaira Therapeutics · Computational Protein Design

Where: Seattle, Washington, United States; South San Francisco, California, United States
Posted: Aug 28

Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London.

As an AI Scientist Intern on our Computational Protein Design team, you will work alongside talented scientists and engineers developing generative AI models for protein and antibody therapeutic design.

What you'd do

Develop and apply deep learning methods for protein/antibody structure, sequence, or property modeling, under the guidance and mentorship of experienced scientists and engineers
Implement and train models on GPUs using PyTorch
Contribute to ongoing research projects involving protein structure, sequence, or biophysical/affinity datasets
Participate in discussions to help generate innovative ideas for advancing AI methodologies in computational protein design
Document findings and communicate progress effectively to peers and mentors

What they want

Currently pursuing a MS or PhD in Computer Science, Machine Learning, or a related technical field, with strong publication record
Hands-on experience with PyTorch and training/inference of AI models on GPUs
Strong interest in AI innovation and its applications to interdisciplinary fields such as biology and chemistry
Extensive hands-on experience with deep learning methods and frameworks
Ability to work collaboratively in a team environment and learn from experienced mentors
A scientifically curious mindset with a passion for exploring new challenges
Experience with large-scale distributed training and inference is a plus
Exposure to molecular structure or biological sequence data or computational biology/bioinformatics is a plus, but not required
Prior research experience demonstrated through publications and/or significant open source code authorship
Interest in contributing to open-source deep learning libraries and frameworks is a plus