Xaira Therapeutics is leveraging AI to transform drug discovery and development by creating generative AI models for protein and antibody therapeutics, and foundation models for biology and disease to enable better target elucidation and patient stratification, ultimately aiming to identify novel therapies and improve success in drug development.
Requirements
- Proficiency in programming languages such as Python, and experience with relevant libraries, toolchain and frameworks for AI model development.
- Strong understanding of deep learning fundamentals, including neural network architectures, optimization techniques, and evaluation metrics.
- Experience applying LLM + RAG to biomedical or healthcare use cases.
- Knowledge of multimodal model development (text, omics, imaging).
- Experienced in PyTorch, training and inference of large models on GPUs
- Hands-on experience with foundation models or large-scale self-supervised learning (e.g., transformers, variational autoencoders).
- Strong expertise in single-cell data analysis, particularly perturb-seq
Responsibilities
- leading the development of generative AI models to design protein and antibody therapeutics
- developing foundation models for biology and disease
- enabling the creation of medicines against historically hard-to-drug molecular targets
- enabling better target elucidation and patient stratification
- continually enable the identification of novel therapies
- improve success in drug development
- implementing and deploying AI models in production environments
Other
- Minimum of two years of industry experience in AI, machine learning, or related roles, with a track record of successfully implementing and deploying AI models in production environments.
- Familiarity with drug discovery processes, computational chemistry, or bioinformatics is desirable.
- Excellent problem-solving skills, with a keen eye for detail and a passion for tackling complex challenges in healthcare.
- Publication track record at conferences such as NeurIPS, ICML, ICLR, etc.
- Track record of AI Innovation demonstrated through publications in Artificial Intelligence/Machine Learning or projects applying them to interdisciplinary fields
