PhD Residency - Foundation Models Human Health, Early Stage Project

Internship - Mountain View, CA

This project aims to revolutionize the development of new therapies by building highly predictive AI systems that bridge the gap between early-stage laboratory research and human clinical outcomes. By leveraging massive clinical biobanks and foundation models, we seek to fundamentally reduce the reliance on traditional preclinical methods and accelerate the global transition toward human-relevant precision medicine.

• Architect foundation models for human health by training Deep Neural Networks on massive, multi‑modal biobank data to uncover novel disease patterns and patient‑specific markers.
• Design agentic frameworks to mine and synthesize findings from heterogeneous biological and clinical datasets to improve the predictive accuracy of human physiological responses.
• Integrate multi‑modal data streams—including omics data, clinical data, and high‑content imaging—into modeling pipelines to correlate experimental results with long‑term clinical outcomes.
• Research and implement modeling approaches to identify personalized therapeutic pathways and address systemic gaps in existing medical research, particularly for under‑represented patient populations.

This project aims to push the limits of science and modeling as we know them and to prove how ML can radically accelerate our understanding of the world.

• Location:
  
  X's headquarters in Mountain View, CA
• Start Date(s):
  Flexible, Early to Mid‑2026
• Duration: A flexible 6‑month to 1‑year program based on project team needs and your availability.

Throughout your AI Residency you can expect:

• To be embedded into one of our confidential or public X projects
• To get paid competitively and receive benefits
• To be part of a lively community of AI and ML Residents
• To attend tech‑talks with AI leaders from across X

• Education:
  
  Currently enrolled in a PhD program in Computer Science, Artificial Intelligence, Computational Biology, Bioinformatics, or a related quantitative field.
• Technical skills:
  • A strong research background in training Deep Neural Networks (e.g., Transformer‑based architectures) on domain‑specific datasets, ranging from omics data, clinical data, to high‑content imaging.
  • Proven ability to implement and fine‑tune vision models (CNNs, ViTs) for tasks like medical image classification and segmentation.
  • Proficiency in Python and modern ML tools and frameworks (e.g., PyTorch, JAX, Hugging Face, Lang Chain)
• Biobank Data Proficiency:
  Practical experience working with large‑scale clinical repositories (e.g., UK Biobank) and managing the computational challenges of data processing and feature engineering and extraction.
• Ambiguity Tolerance:
  The ability to thrive in a high‑velocity environment characterized by rapid iteration to solving complex technical hurdles.
• Can Do Attitude:
  Willing to learn new tools and enthusiasm in becoming an effective thought partner with project teams.
• Cross‑Functional
  
  Collaboration:
  
  Strong team player with excellent verbal and written communication skills (e.g. experience writing technical design docs, knowledge sharing presentations).

• Mechanistic/Physics‑based modeling experience:
  Proficiency in developing and calibrating multi‑scale computational models to simulate tissue growth.
• Research & Open‑Source Portfolio:
  Open‑source projects that demonstrate relevant skills and/or publications in relevant conferences and journals.
• Knowledge Graph Expertise:
  Familiarity with constructing biological knowledge graphs to map complex interactions and systemic physiological cascades.
• Distributed Systems & Infrastructure:
  Experience in using distributed computing resources, such as GCP, AWS, Azure or HPC clusters.
• Mission Alignment: A passion for leveraging AI to improve health equity and solve long‑standing ethical challenges in biomedical research.

Salary: The US base salary range for this position is $109,000 - $150,000 + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant…