Product Builder Computational Scientist Biologist

Maximilian Lombardo

I build products and systems for scientific discovery, turning advances in biological computing into tools scientists can use.

Explore my work

00 About

Translating research into products.

I currently work at Biohub (previously the Chan Zuckerberg Initiative) at the intersection of product, AI, and biology. Before that I worked on computational target discovery at Kallyope, and before that on lymphoma therapeutics at Columbia.

My path from the lab to computational science to product still shapes how I approach the work. It lets me engage with research early, understand what is technically possible, and help shape what becomes a product. That often means working alongside researchers before there is a finished model, a clear use case, or even a settled product direction.

01 Selected work

VariantFormer

Biohub · 2025

VariantFormer predicts how an individual's genetic variants affect gene expression across tissues. For its release, I built the technical product and GTM layer around the model: an interactive playground, analysis notebook, explainer, and launch materials. The case study visualizes predictions for 17,859 genes across 63 tissues.

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CELLxGENE

CZI · Product and researcher workflows

Over roughly three years in product, I shaped how researchers discover, explore, and annotate single-cell data. The work connects chromatin-accessibility discovery, cell-type prediction, Census interviews, and workshops with real datasets: understanding what researchers need, defining useful tools, and learning from their use.

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02 Experience

Biohub / CZI

2020 – PRESENT

I work across product, AI research, and cell science at Biohub. I have helped shape CELLxGENE for more than 10,000 researchers, led the release of nine biological foundation models on the Virtual Cells Platform, and worked directly with research teams to turn emerging model capabilities into product use cases, demos, and roadmap decisions. My work also spans model evaluation and biological AI safety. Biohub in detail →

Kallyope

2017 – 2020

Computational biology and machine learning for drug discovery. I led the analysis of a single-cell atlas that helped secure a $2M milestone with Novo Nordisk, built ML pipelines across multiple discovery programs, and contributed to work that progressed into Kallyope’s first clinical programs. I also co-authored research from the company’s gut-brain biology program in Cell Metabolism. Kallyope in detail →

Earlier

2013 – 2017

Before drug discovery and product, I worked in experimental therapeutics at Columbia, studying mechanisms of drug resistance in lymphoma and contributing to four publications. I later moved into computational science at the University of Amsterdam, including a research internship at IBM where I worked on machine learning for multi-omics and single-cell data. Earlier work and thesis →

03 Products

Digital Biology
digitalbio.org

Where biology
meets technology.

A free email briefing I built to follow AI, biotech, and drug discovery.
Explore Digital Biology