Job description

About Xaira Therapeutics 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. About the Role 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. During your internship, you will contribute to advancing state-of-the-art machine learning models for biology, with a focus on impacting protein/antibody design and drug discovery. You will also have the opportunity to collaborate with interdisciplinary experts in biology, drug discovery, and clinical research. Responsibilities 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 Qualifications 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 Internship Duration 3 months The expected monthly compensation range is $10,000–$15,000, depending on experience and qualifications. We are open to higher compensation for candidates with exceptional experience or impact. This internship is designed to provide a unique learning experience, offering hands-on exposure to the intersection of AI and computational protein design while allowing you to contribute meaningfully to real-world projects.

Requirements

Must-haves

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
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
Prior research experience demonstrated through publications and/or significant open source code authorship

Nice-to-haves

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
Interest in contributing to open-source deep learning libraries and frameworks is a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learning

Platform: greenhouse
Effort: quick