About Arc Institute

The Arc Institute is a new scientific institution that conducts curiosity-driven basic science and technology development to understand and treat complex human diseases. Headquartered in Palo Alto, California, Arc is an independent research organization founded on the belief that many important research programs will be enabled by new institutional models. Arc operates in partnership with Stanford University, UCSF, and UC Berkeley.

While the prevailing university research model has yielded many tremendous successes, we believe in the importance of institutional experimentation as a way to make progress. These include:

• Funding: Arc will fully fund Core Investigators’ (PIs’) research groups, liberating scientists from the typical constraints of project-based external grants.
• Technology: Biomedical research has become increasingly dependent on complex tooling. Arc Technology Centers develop, optimize and deploy rapidly advancing experimental and computational technologies in collaboration with Core Investigators.
• Support: Arc aims to provide first-class support—operationally, financially and scientifically—that will enable scientists to pursue long-term high risk, high reward research that can meaningfully advance progress in disease cures, including neurodegeneration, cancer, and immune dysfunction.
• Culture: We believe that culture matters enormously in science and that excellence is difficult to sustain. We aim to create a culture that is focused on scientific curiosity, a deep commitment to truth, broad ambition, and selfless collaboration.

Arc has scaled to over 350 people to date. With $650M+ in committed funding and a state of the art new lab facility in Palo Alto, Arc will continue to grow quickly in the coming years.

About the Position

We are searching for an exceptional scientific leader to establish a new team within Arc Institute’s Computational Technology Center, serving as Machine Learning Research Lead, for our Alzheimer's Disease Initiative (ADI).

This ambitious initiative spans Arc's Technology Centers and Core Investigator Laboratories and focuses on high-throughput interrogation of neurodegeneration and Alzheimer's disease mechanisms using advanced gene editing and functional genomics approaches. As the Machine Learning Research Lead, ADI, you will spearhead development of sophisticated machine learning foundation models to capture cell states and infer gene regulatory networks and causal relationships to predict therapeutic interventions.

This position offers the rare opportunity to build and lead a world-class team while making direct contributions to understanding and potentially treating Alzheimer's disease through state-of-the-art computational biology and machine learning approaches.

About You

• You are passionate about machine learning and computational biology , with expertise in applying cutting edge ML approaches to biological systems
• You excel at developing interpretable machine learning approaches , such as variational inference and causal modeling methods

In This Position, You Will

• Attract, build and lead a team of exceptional machine learning research scientists dedicated to developing foundation models for cellular systems in Alzheimer's disease
• Develop and execute on a roadmap of interpretable machine learning approaches to understand disease mechanisms, with emphasis on variational inference, causal modeling, as well as modern transformer- and diffusion-based architectures
• Work closely with experimentalists on brain organoid/spheroid cellular models as well as models, working with scRNA-seq, Perturb-seq and other datasets to unravel causal gene pathways relevant to Alzheimer’s disease

Required Qualifications

• PhD in Computational Biology, Bioinformatics, Machine Learning, Computer Science, or related quantitative field

The base salary range for this position is $338,500 to $400,500. These amounts reflect the range of base salary that the Institute reasonably would expect to pay a new hire or internal candidate for this position. The actual base compensation paid to any individual for this position may vary depending on factors such as experience, market conditions, education/training, skill level, and whether the compensation is internally equitable, and does not include bonuses, commissions, differential pay, other forms of compensation, or benefits. This position is also eligible to receive an annual discretionary bonus, with the amount dependent on individual and institute performance factors.