Director Machine Learning Alzheimer's Disease Initiative
Independent nonprofit research institute building AI-and-biology tools and conducting biomedical research on complex diseases.
About Arc Institute
Arc Institute is a Palo Alto-based independent nonprofit founded in 2021. It supports long-term biomedical research and develops experimental, computational, and machine-learning technologies, including virtual-cell and genomic foundation models, in partnership with Stanford University, UC Berkeley, and UCSF.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will establish and lead a machine learning research team focused on Alzheimer's disease. You will develop foundation models and interpretable approaches, collaborate with experimentalists on biological datasets, guide predictive modeling, build external collaborations, and publish research through papers, open-source code, and conference presentations.
Requirements
- PhD in Computational Biology, Bioinformatics, Machine Learning, Computer Science, or a related quantitative field
- 7+ years of relevant experience
- At least 3 years of people management experience
- Research experience in academic, research institute, biotech, or pharmaceutical settings
- Experience applying machine learning to biological datasets, single-cell profiling data, and foundation models
- Experience with interpretable machine learning approaches such as variational inference
- Advanced PyTorch skills and ideally model training at scale experience
- Publications in top-tier computational biology and machine learning journals
- Ability to lead, mentor, manage, and remain technically hands-on
Responsibilities
- Build and lead a machine learning research team
- Develop a roadmap for interpretable machine learning approaches to disease mechanisms
- Develop foundation models, variational inference, causal models, transformer architectures, and diffusion architectures
- Work with experimentalists on brain organoid, spheroid, and in vivo models
- Analyze scRNA-seq, Perturb-seq, and related datasets
- Develop models that predict how perturbations move cell states toward lower-risk profiles
- Ensure models are grounded in disease biology and inform experimental strategies
- Foster external computational biology and neuroscience collaborations
- Publish research through preprints, journals, open-source code, and conference presentations
Benefits
- Annual discretionary bonus