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Full Time Permanent

Washington, Washington DC · USA job

Staff AI Scientist, Foundation Models

Tempus

Washington, Washington DC

Job description

Overview

As Staff AI Scientist, Foundation Models at Tempus, you advance large-scale foundation models for precision medicine. You'll lead technical direction within a multidisciplinary AI team, turning research into validated, deployable solutions. Your work spans multimodal clinical and molecular data to applications in prognosis, treatment outcomes, and biomarker discovery. This role blends ML research with clinical impact, offering exposure to real-world healthcare challenges and scalable AI in biomedicine.

Compensation / Benefits
  • incentive compensation
  • restricted stock units
  • medical and other benefits
  • competitive salary range
  • full-time position
  • remote - USA option
Responsibilities
  • Provide senior technical leadership and shape model architecture, research direction, and execution
  • Design, train, and advance state-of-the-art foundation models on multimodal clinical and molecular data
  • Develop downstream applications such as prognosis, outcome prediction, patient stratification, and biomarker discovery
  • Contribute original ideas to Tempus's foundation-model agenda and engage with the AI research community
  • Collaborate with cross-functional teams to ensure models meet quality, validation, and regulatory standards for healthcare
  • Translate research into validated products and partner-specific AI solutions
Key requirements
  • Ph.D. in Computer Science or related quantitative field
  • 3+ years of industry experience in end-to-end ML model development
  • Expertise in large-scale deep learning and foundation models (pretraining, representation learning, self-supervised, multimodal)
  • Experience with biomedical modalities (pathology, genomics, longitudinal EHR) is preferred
  • Strong publication record in ML, CV, NLP, or related AI venues; medical AI publications preferred
  • Proficiency in Python and PyTorch; solid software engineering practices
  • Technical leadership
  • Cross-functional collaboration
  • Research communication
  • Python
  • PyTorch
  • Foundation-model development

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