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.

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 The position is a full-time appointment jointly housed in Columbia’s Irving Institute for Cancer Dynamics and The Fu Foundation School of Engineering & Applied Science.

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.

Required qualifications

- B.S./B.E. (minimum) in Computer Science, Biomedical/Electrical Engineering, Statistics,



Bioinformatics, Applied Math, or related field

- 6+ years of experience in software engineering

- 3+ yrs hands-on experience training generative AI or large-language models at scale

- Substantial expertise in training deep learning models and tuning large foundation models.

- Expertise with developing efficient data loaders for large datasets and optimizing training workflows.

- Deep knowledge of probabilistic modelling, self-supervised learning and representation learning, diffusion/VAE/flow matching/transformer architectures

- Strong Python, PyTorch/JAX, containerization & MLOps skills; familiarity with distributed training and contemporary experiment-tracking stacks

- Experience with AI coding tools (e.g., Copilot, Cursor)

Preferred extras

- M.S. or graduate-level degree in relevant field

- Experience with single-cell and spatial genomic or imaging data, and multimodal integration

- Expertise in statistical causal discovery and inference

- Publications or open-source contributions in generative models

- Strong interest in applications and driving impact in cancer biology and immunology

📌 Staff Associate III (Morningside)
🏢 Columbia University in the City of New York
📍 Morningside