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 modern 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

Please submit a single PDF containing a cover letter, CV, links to code/publications, and contact information for three references

Applications will be reviewed on a rolling basis until the roles are filled.

Columbia University is an Equal Opportunity Employer / Disability / Veteran

Pay Transparency Disclosure

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training.  The above hiring range represents the University?s good faith and reasonable estimate of the range of possible compensation at the time of posting.

About Columbia University

Columbia University is one of the world's most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields. The University recognizes the importance of its location in New York City and seeks to link its research and teaching to the vast resources of a great metropolis. It seeks to attract a diverse and international faculty and student body, to support research and teaching on global issues, and to create academic relationships with many countries and regions. It expects all areas of the university to advance knowledge and learning at the highest level and to convey the products of its efforts to the world.

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