Postdoctoral Researcher, Konermann & Goodarzi Labs

About Arc Institute

The Arc Institute is a new scientific institution conducting 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 fully funds 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 seeking an exceptional computational postdoctoral fellow to join the Konermann and Goodarzi laboratories at the Arc Institute. This is a unique opportunity to work at the intersection of AI/ML, functional genomics, precision medicine, and single cell multiomics to develop and apply cutting-edge machine learning tools for understanding causal drivers of complex biological systems.

In this joint position, you will contribute to developing broadly applicable computational frameworks for integrating multi-omics data, building predictive models of cellular behavior, and scaling phenotypic discovery in disease-relevant contexts, such as Alzheimer’s disease. Your work will leverage Arc’s state-of-the-art experimental platforms and collaborative environment.

About You

• You are passionate about using machine learning and computational approaches to tackle challenging problems in human biology and disease.

• You are excited to develop novel computational methods and frameworks that enable biological discovery at scale.

• You are creative and eager to move with the fast-paced nature of modern AI/ML, and to explore new approaches beyond traditional methods.

• You enjoy working across disciplinary boundaries and integrating diverse data types.

• You work efficiently and write clean, well-documented code. You value reproducibility and good software engineering practices.

• You thrive in a fast-paced, collaborative environment where you can drive multiple projects forward in parallel.

In This Position, You Will

• Develop and apply AI/ML frameworks for modeling multi-omics datasets, with emphasis on scalable approaches for phenotypic screening and disease modeling.

• Design and implement causal machine learning methods for predicting cellular states, drug responses, and disease phenotypes from genomic data.

• Collaborate closely with experimental teams to design studies, optimize protocols, and integrate computational and experimental workflows.

• Present research findings at internal meetings, seminars, and external conferences.

• Mentor junior researchers, graduate students, and contribute to Arc’s collaborative scientific culture.

• Publish high-impact research in leading scientific journals.

Requirements

• PhD in Machine Learning, Computational Biology, Bioinformatics, Computer Science, Statistics, Bioengineering, or a related quantitative field.

• Strong publication record demonstrating expertise in computational analysis of genomics data, particularly single-cell technologies.

• Extensive experience with Python, machine learning frameworks, and distributed training, including active and well-maintained projects on public repositories.

• Experience with machine learning and statistical modeling, particularly applied to biological data.

• Strong understanding of molecular biology, with ability to design analyses that directly address biological questions.

• Proven ability to work independently and collaboratively in interdisciplinary teams.

• Excellent written and oral communication skills, with demonstrated ability to present complex computational work to diverse audiences.

• Track record of completing projects and publishing results in peer-reviewed journals.

Preferred Qualifications

• Experience with pooled screening technologies (CRISPR screens, perturbation screens, drug screens) and associated computational modeling.

• Experience with multi-omics data integration and analysis.

• Experience with cloud computing and scalable data analysis workflows.

• Contributions to open-source software or publicly available computational tools.

• Experience with spatial transcriptomics or other emerging single-cell technologies.

The minimum base salary for this position is $80,000. Base salary for this role is determined by how many months of relevant postdoctoral experience a successful candidate has. Base salary for this role is not negotiable.