AI Scientist - Biomedical Multimodal Modeling

<div class="content-intro"><p><strong>About Xaira Therapeutics</strong></p> <p>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.</p></div><div><strong>About the Role</strong><br>We are building foundation models for biological systems, focusing on learning from complex, multimodal data.&nbsp;Our approach combines representation learning and generative modeling to capture structure, variation, and latent organization in high-dimensional settings. We work with both image-based data and high-dimensional molecular (omics) measurements, treating them as complementary views of the same underlying system.&nbsp;Our goal is to develop models that can both encode rich representations and generate or infer missing aspects from partial observations.<br><br><strong>What You’ll Do</strong></div> <ul> <li>Develop multimodal foundation models spanning image and molecular data</li> <li>Design approaches that unify representation learning and generative modeling</li> <li>Train large-scale models on structured, high-dimensional datasets</li> <li>Explore cross-modal alignment, prediction, and generation</li> <li>Build scalable systems for data processing, training, and evaluation</li> <li>Work in a research-driven environment with evolving problem definitions</li> </ul> <div><br><strong>What We’re Looking For<br><br></strong></div> <div>Strong background in deep learning, especially in modern architecture</div> <ul> <li>Experience with representation learning (self-supervised, contrastive, masked modeling, etc.)</li> <li>Experience with generative modeling (diffusion, autoregressive models, VAEs, or related approaches)</li> <li>Proficiency in Python, ML frameworks, and CI/CD (PyTorch, JAX, etc.)</li> </ul> <div>Relevant Experience</div> <ul> <li>Work on multimodal learning or cross-domain representation learning</li> <li>Experience with image data, molecular / omics-like data, or both</li> <li>Familiarity with large-scale training</li> </ul> <div>Bonus</div> <ul> <li>Exposure to biological or sci