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Salary: USD 220,000 - 260,000 / annual
Tempus is building a precision medicine platform that connects real-world clinical and molecular data to deliver actionable insights for physicians. The Foundation Model group develops large-scale AI models across diverse clinical modalities—longitudinal patient data, pathology, genomics, and biomedical datasets—to power applications in clinical care, biomedical research, and drug development.
As a Staff AI Scientist, Foundation Models, you will be a senior individual contributor advancing state-of-the-art foundation models for precision medicine. You will work at the intersection of machine learning, clinical science, and life sciences, leveraging one of the world's largest clinical and molecular datasets.
Key Responsibilities:
- Provide strong hands-on technical contributions while shaping model architecture, research direction, and team execution.
- Design, train, and advance foundation models across large-scale multimodal clinical and molecular data, including longitudinal patient records, pathology, genomics, and related biomedical modalities.
- Drive innovation in pretraining, representation learning, self-supervised learning, multimodal learning, model scaling, and post-training techniques.
- Develop and adapt foundation-model-derived models for downstream applications: prognosis, treatment outcome prediction, patient stratification, biomarker discovery, clinical trial applications, and partner-specific solutions.
- Contribute original research ideas, publish impactful work, and engage with the broader AI research community.
- Work with cross-functional teams to ensure models and AI products meet quality, validation, and regulatory standards for healthcare and life sciences.
Requirements:
- Ph.D. in Computer Science or a related quantitative field.
- 3+ years of industry experience driving end-to-end development of machine learning models, from research and experimentation to validation and production deployment.
- Demonstrated expertise in large-scale deep learning and foundation model development, including experience in pretraining, representation learning, self-supervised learning, multimodal modeling, or post-training.
- Strong track record of original, high-impact research demonstrated through peer-reviewed publications in leading machine learning, computer vision, NLP, or related AI venues.
- Proficiency in Python and the modern deep learning stack (e.g., PyTorch).
- Strong product-level coding skills for model development and solid understanding of industry-standard software engineering practices.
- Experience with computer vision, NLP, pathology, longitudinal EHR, genomics, or other biomedical modalities is preferred.
- Prior work in medical or healthcare AI is highly valued but not required.
- Publications in medical or healthcare AI and prior experience developing foundation models are strongly preferred.
