Scientist, Computational Biology

About Arc Institute

Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.

Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.

Our two Institute Initiatives reflect this model in action:

• Virtual Cell Initiative: Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets,  accelerating the path from biological insight to clinical trials.
• Alzheimer's Disease Initiative: Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.

More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.

About the position

We are hiring for two Scientist positions on Arc's Computational Technology Center, each turning large-scale perturbational and single-cell datasets into mechanistic biological insight:

1. Perturb-seq / functional genomics track — focused on large-scale CRISPR screen and Perturb-seq analysis, contributing to the Virtual Cell Initiative (VCI).
2. Neurobiology / microglia track — focused on single-cell analysis of microglia and neurodegeneration, contributing to the Alzheimer's Disease Initiative (ADI).

Please indicate which focus area you're applying for in your application.

Situated at the interface of functional genomics, computational biology, and machine learning, successful candidates will analyze and model data from Perturb-seq, single-cell and multi-omic sequencing, lineage tracing, chemogenetic screens, and related high-throughput experimental approaches.

Both roles are highly collaborative, partnering closely with experimental scientists, bioinformatics infrastructure teams, machine learning researchers, and Arc investigators to identify biological mechanisms, nominate targets, and guide the design of future experiments.