We are launching a campus-wide initiative to build foundation models that simulate the evolution of tumor ecosystems. You will be the lead engineer contributing to large-scale generative modelling on single-cell, spatial-omics, and clinical data.You will collaborate daily with a diverse team of AI / ML researchers, computational biologists, clinicians and bioengineers who share a mission of transforming our understanding of cancer progression and improving its treatment through next-generation AI and experimental platforms.
Core responsibilities
- Design, train, and deploy multi-modal foundation models for single-cell and spatial cancer data
- Build scalable training pipelines in PyTorch / JAX on GPU clusters and cloud HPC / ADK
- Implement data-efficient fine-tuning, adaptive learning workflows and agentic frameworks for reasoning
- Collaborate with machine learning experts and computational biologists to build tools for AI agents e.g. libraries, MCPs and APIs
Preferred extras
M.S. or graduate-level degree in relevant fieldExperience with single-cell and spatial genomic or imaging data, and multimodal integrationExpertise in statistical causal discovery and inferencePublications or open-source contributions in generative modelsStrong interest in applications and driving impact in cancer biology and immunology