AI Scientist

Role Overview
The team is seeking an AI Scientist who specializes in multimodal data. This role involves designing and building models that learn from and reason across diverse biological, clinical, and real world datasets. You will develop ML systems that combine multi omic assays, clinical records, biospecimen linked measurements, and digital phenotyping signals to better understand disease biology and support work in diagnostics and therapeutics. This role is well suited for individuals who want to advance ML methods for complex and heterogeneous biomedical data.

Key Responsibilities

• Develop advanced multimodal ML architectures including Transformers, multimodal generative models, and graph based models that can learn from genomics, transcriptomics, proteomics, clinical EHRs, wearable data, symptom logs, and biospecimen datasets.
• Build models that capture biological and clinical complexity in neuroimmune conditions to support biomarker discovery, patient stratification, drug repurposing, and diagnostic development.
• Work across varied data types including structured, unstructured, temporal, and high dimensional biological datasets to create strong and scalable modeling pipelines.
• Collaborate with engineering, clinical, and scientific teams to ensure the availability of high quality ML ready multimodal datasets.
• Conduct exploratory analysis and develop interpretable ML methods that reveal biologically meaningful patterns and deepen understanding of disease mechanisms.
• Deploy models into internal workflows and contribute to the team's growing library of proprietary ML capabilities.
• Present research outcomes to cross functional and external scientific partners.

Required Qualifications

• PhD or MS plus relevant experience in Machine Learning, Computational Biology, Bioinformatics, Computer Science, Applied Mathematics, or a related discipline.
• Demonstrated expertise in multimodal data modeling, representation learning, multi omic ML, or clinical and biomedical machine learning.
• Hands on experience with one or more of the following focus areas: Multi omic modeling, clinical and EHR modeling, longitudinal and time series modeling, generative modeling such as VAEs or diffusion models.
• Proficiency in Python and modern ML frameworks such as PyTorch or TensorFlow.
• Ability to collaborate effectively with technical, clinical, and biological stakeholders.

Preferred Qualifications

• Familiarity with chronic, neuroimmune, or rare disease biology.
• Experience working with large scale biomedical datasets including biospecimen linked sources.
• Experience with cloud computing, distributed model training, or MLOps best practices.
• Strong intuition for identifying biological signal within complex datasets.